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MARC Lista (tabellare)
Advances in compositional data analysis : festschrift in honour of Vera Pawlowsky-Glahn / / Peter Filzmoser, Karel Hron, Josep Antoni Martín-Fernández, Javier Palarea-Albaladejo, editors
Advances in compositional data analysis : festschrift in honour of Vera Pawlowsky-Glahn / / Peter Filzmoser, Karel Hron, Josep Antoni Martín-Fernández, Javier Palarea-Albaladejo, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (XVIII, 404 p. 113 illus., 91 illus. in color.)
Disciplina 519.5
Altri autori (Persone) Pawlowsky-GlahnVera
Soggetto topico Estadística matemàtica
Investigació quantitativa
Mathematical statistics
Quantitative research
Soggetto genere / forma Llibres electrònics
ISBN 3-030-71175-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface -- J.J. Egozcue and W.L. Maldonado: An interpretable orthogonal decomposition of positive square matrices -- Part I Fundamentals -- I. Erb and N. Ay: The information-geometric perspective of compositional data analysis -- D.R. Lovell: Log-ratio analysis of finite precision data: caveats, and connections to digital lines and number theory -- G. Mateu-Figueras, G.S. Monti and J.J. Egozcue: Distributions on the simplex revisited -- J. Graffelman: Compositional biplots: a story of false leads and hidden features revealed by the last dimensions -- Part II Statistical Methodology -- K. Fačevicová, P. Kynčlová and K. Macků: Geographically weighted regression analysis for two-factorial compositional data -- C. Barceló-Vidal and J.A. Martín-Fernández: Factor analysis of compositional data with a total -- M. Gallo, V. Simonacci and V. Todorov: A compositional three-way approach for student satisfaction analysis -- M. Templ: Artificial neural networks to impute rounded zeros in compositional data -- E. Saus–Sala, À. Farreras–Noguer, N. Arimany–Serrat, and G. Coenders: Compositional du pont analysis. A visual tool for strategic financial performance assessment -- A. Menafoglio: Spatial statistics for distributional data in Bayes spaces: from object-oriented kriging to the analysis of warping functions -- C. Thomas-Agnan, T. Laurent, A. Ruiz-Gazen, N. Thi Huong An, R. Chakir and A. Lungarska: Spatial simultaneous autoregressive models for compositional data: application to land use -- Part III Applications -- A. Buccianti, C. Gozzi: The whole versus the parts: the challenge of compositional data analysis (CoDA) methods for geochemistry -- M.A. Engle and J.A. Chaput: Groundwater origin determination in historic chemical datasets through supervised compositional data analysis: Brines of the Permian Basin, USA -- J.M. McKinley, U. Mueller, P.M. Atkinson, U. Ofterdinger, S.F. Cox, R. Doherty, D. Fogarty and J.J. Egozcue -- Chronic kidney disease of uncertain aetiology and its relation with waterborne environmental toxins: An investigation via compositional balances -- R.A. Olea, J.A. Martín-Fernández and W.H. Craddock: Multivariate classification of the crude oil petroleum systems in southeast Texas, USA, using conventional and compositional data analysis of biomarkers -- J.R. Wu, J.M. Macklaim, B.L. Genge and G.B. Gloor: Finding the centre: compositional asymmetry in high-throughput sequencing datasets -- L. Huang and H. Li: Bayesian balance-regression in microbiome studies using stochastic search -- D.E. McGregor, P.M. Dall, J. Palarea-Albaladejo and S.F.M. Chastin: Compositional data analysis in physical activity and health research. Looking for the right balance -- D. Dumuid, Ž. Pedišić, J. Palarea-Albaladejo, J.A. Martín-Fernández, K. Hron and T. Olds: Compositional data analysis in time-use epidemiology.
Record Nr. UNISA-996466397003316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advances in compositional data analysis : festschrift in honour of Vera Pawlowsky-Glahn / / Peter Filzmoser, Karel Hron, Josep Antoni Martín-Fernández, Javier Palarea-Albaladejo, editors
Advances in compositional data analysis : festschrift in honour of Vera Pawlowsky-Glahn / / Peter Filzmoser, Karel Hron, Josep Antoni Martín-Fernández, Javier Palarea-Albaladejo, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (XVIII, 404 p. 113 illus., 91 illus. in color.)
Disciplina 519.5
Altri autori (Persone) Pawlowsky-GlahnVera
Soggetto topico Estadística matemàtica
Investigació quantitativa
Mathematical statistics
Quantitative research
Soggetto genere / forma Llibres electrònics
ISBN 3-030-71175-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface -- J.J. Egozcue and W.L. Maldonado: An interpretable orthogonal decomposition of positive square matrices -- Part I Fundamentals -- I. Erb and N. Ay: The information-geometric perspective of compositional data analysis -- D.R. Lovell: Log-ratio analysis of finite precision data: caveats, and connections to digital lines and number theory -- G. Mateu-Figueras, G.S. Monti and J.J. Egozcue: Distributions on the simplex revisited -- J. Graffelman: Compositional biplots: a story of false leads and hidden features revealed by the last dimensions -- Part II Statistical Methodology -- K. Fačevicová, P. Kynčlová and K. Macků: Geographically weighted regression analysis for two-factorial compositional data -- C. Barceló-Vidal and J.A. Martín-Fernández: Factor analysis of compositional data with a total -- M. Gallo, V. Simonacci and V. Todorov: A compositional three-way approach for student satisfaction analysis -- M. Templ: Artificial neural networks to impute rounded zeros in compositional data -- E. Saus–Sala, À. Farreras–Noguer, N. Arimany–Serrat, and G. Coenders: Compositional du pont analysis. A visual tool for strategic financial performance assessment -- A. Menafoglio: Spatial statistics for distributional data in Bayes spaces: from object-oriented kriging to the analysis of warping functions -- C. Thomas-Agnan, T. Laurent, A. Ruiz-Gazen, N. Thi Huong An, R. Chakir and A. Lungarska: Spatial simultaneous autoregressive models for compositional data: application to land use -- Part III Applications -- A. Buccianti, C. Gozzi: The whole versus the parts: the challenge of compositional data analysis (CoDA) methods for geochemistry -- M.A. Engle and J.A. Chaput: Groundwater origin determination in historic chemical datasets through supervised compositional data analysis: Brines of the Permian Basin, USA -- J.M. McKinley, U. Mueller, P.M. Atkinson, U. Ofterdinger, S.F. Cox, R. Doherty, D. Fogarty and J.J. Egozcue -- Chronic kidney disease of uncertain aetiology and its relation with waterborne environmental toxins: An investigation via compositional balances -- R.A. Olea, J.A. Martín-Fernández and W.H. Craddock: Multivariate classification of the crude oil petroleum systems in southeast Texas, USA, using conventional and compositional data analysis of biomarkers -- J.R. Wu, J.M. Macklaim, B.L. Genge and G.B. Gloor: Finding the centre: compositional asymmetry in high-throughput sequencing datasets -- L. Huang and H. Li: Bayesian balance-regression in microbiome studies using stochastic search -- D.E. McGregor, P.M. Dall, J. Palarea-Albaladejo and S.F.M. Chastin: Compositional data analysis in physical activity and health research. Looking for the right balance -- D. Dumuid, Ž. Pedišić, J. Palarea-Albaladejo, J.A. Martín-Fernández, K. Hron and T. Olds: Compositional data analysis in time-use epidemiology.
Record Nr. UNINA-9910484712403321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied probability : from random experiments to random sequences and statistics / / Valérie Girardin and Nikolaos Limnios
Applied probability : from random experiments to random sequences and statistics / / Valérie Girardin and Nikolaos Limnios
Autore Girardin Valérie
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (265 pages)
Disciplina 519.2
Soggetto topico Distribution (Probability theory)
Statistics
Stochastic processes
Probabilitats
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-97963-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Notation -- 1 Events and Probability Spaces -- 1.1 Sample Space -- 1.2 Measure Spaces -- 1.2.1 σ-Algebras -- Properties of σ-Algebras -- 1.2.2 Measures -- Properties of Measures -- Dirac Measure -- Counting Measure -- Lebesgue Measure -- 1.3 Probability Spaces -- 1.3.1 General Case -- 1.3.2 Conditional Probabilities -- 1.3.3 Discrete Case: Combinatorial Analysis and Entropy -- Properties of Shannon Entropy -- 1.4 Independence of Finite Collections -- 1.5 Exercises -- 2 Random Variables -- 2.1 Random Variables -- 2.1.1 Measurable Functions -- Properties of Measurable Functions -- 2.1.2 Distributions and Distribution Functions -- Properties of Distribution Functions -- Properties of Quantiles -- 2.2 Expectation -- 2.2.1 Lebesgue Integral -- Properties of Lebesgue Integrals -- 2.2.2 Expectation -- 2.3 Discrete Random Variables -- 2.3.1 General Properties -- 2.3.2 Classical Discrete Distributions -- Dirac Distribution -- Uniform Distribution -- Bernoulli Distribution -- Binomial Distribution -- Hyper-Geometric Distribution -- Geometric and Negative Binomial Distributions -- Poisson Distribution -- 2.4 Continuous Random Variables -- 2.4.1 Absolute Continuity of Measures -- 2.4.2 Densities -- Properties of Densities of Random Variables -- 2.4.3 Classical Distributions with Densities -- Uniform Distribution -- Gaussian Distribution -- Gamma, Exponential, Chi-Squared, Erlang Distributions -- Log-Normal Distribution -- Weibull Distribution -- Inverse-Gaussian Distribution -- Beta Distribution -- Fisher Distribution -- Student and Cauchy Distributions -- 2.4.4 Determination of Distributions -- 2.5 Analytical Tools -- 2.5.1 Generating Functions -- Properties of Generating Functions -- 2.5.2 Fourier Transform and Characteristic Functions -- Properties of Characteristic Functions -- 2.5.3 Laplace Transform.
Properties of Laplace Transforms -- 2.5.4 Moment Generating Functions and Cramér Transform -- Properties of Cramér Transform -- 2.6 Reliability and Survival Analysis -- 2.7 Exercises and Complements -- 3 Random Vectors -- 3.1 Relations Between Random Variables -- 3.1.1 Covariance -- Properties of Covariance and Correlation Coefficients -- 3.1.2 Independence of Random Variables -- 3.1.3 Stochastic Order Relation -- 3.1.4 Entropy -- Properties of Entropy -- 3.2 Characteristics of Random Vectors -- 3.2.1 Product of Probability Spaces -- 3.2.2 Distribution of Random Vectors -- Properties of Multi-dimensional Distribution Functions -- Properties of Densities of Random Vectors -- Properties of Covariance Matrices -- 3.2.3 Independence of Random Vectors -- Properties of Covariance Matrices of Two Vectors -- 3.3 Functions of Random Vectors -- 3.3.1 Order Statistics -- 3.3.2 Sums of Independent Variables or Vectors -- Properties of Convolution -- 3.3.3 Determination of Distributions -- 3.4 Gaussian Vectors -- 3.5 Exercises and Complements -- 4 Random Sequences -- 4.1 Enumerable Sequences -- 4.1.1 Sequences of Events -- Properties of Superior and Inferior Limits of Events -- 4.1.2 Independence of Sequences -- 4.2 Stochastic Convergence -- 4.2.1 Different Types of Convergence -- 4.2.2 Convergence Criteria -- 4.2.3 Links Between Convergences -- 4.2.4 Convergence of Sequences of Random Vectors -- 4.3 Limit Theorems -- 4.3.1 Asymptotics of Discrete Distributions -- 4.3.2 Laws of Large Numbers -- 4.3.3 Central Limit Theorem -- 4.4 Stochastic Simulation Methods -- 4.4.1 Generating Random Variables -- 4.4.2 Monte Carlo Simulation Method -- 4.5 Exercises and Complements -- 5 Introduction to Statistics -- 5.1 Non-parametric Statistics -- 5.1.1 Empirical Distribution Function -- 5.1.2 Confidence Intervals -- 5.1.3 Non-parametric Testing -- 5.2 Parametric Statistics.
5.2.1 Point Estimation -- 5.2.2 Maximum Likelihood Method -- 5.2.3 Precision of the Estimators -- 5.2.4 Parametric Confidence Intervals -- 5.2.5 Testing in a Parametric Model -- 5.3 The Linear Model -- 5.3.1 Linear and Quadratic Approximations -- 5.3.2 The Simple Linear Model -- 5.3.3 ANOVA -- For Two Samples -- One Way Model -- Two Way Model -- 5.4 Exercises and Complements -- Further Reading -- Measure and Probability -- Probability Theory and Statistics -- Applications -- Index.
Record Nr. UNINA-9910568249603321
Girardin Valérie  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied probability : from random experiments to random sequences and statistics / / Valérie Girardin and Nikolaos Limnios
Applied probability : from random experiments to random sequences and statistics / / Valérie Girardin and Nikolaos Limnios
Autore Girardin Valérie
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (265 pages)
Disciplina 519.2
Soggetto topico Distribution (Probability theory)
Statistics
Stochastic processes
Probabilitats
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-97963-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Notation -- 1 Events and Probability Spaces -- 1.1 Sample Space -- 1.2 Measure Spaces -- 1.2.1 σ-Algebras -- Properties of σ-Algebras -- 1.2.2 Measures -- Properties of Measures -- Dirac Measure -- Counting Measure -- Lebesgue Measure -- 1.3 Probability Spaces -- 1.3.1 General Case -- 1.3.2 Conditional Probabilities -- 1.3.3 Discrete Case: Combinatorial Analysis and Entropy -- Properties of Shannon Entropy -- 1.4 Independence of Finite Collections -- 1.5 Exercises -- 2 Random Variables -- 2.1 Random Variables -- 2.1.1 Measurable Functions -- Properties of Measurable Functions -- 2.1.2 Distributions and Distribution Functions -- Properties of Distribution Functions -- Properties of Quantiles -- 2.2 Expectation -- 2.2.1 Lebesgue Integral -- Properties of Lebesgue Integrals -- 2.2.2 Expectation -- 2.3 Discrete Random Variables -- 2.3.1 General Properties -- 2.3.2 Classical Discrete Distributions -- Dirac Distribution -- Uniform Distribution -- Bernoulli Distribution -- Binomial Distribution -- Hyper-Geometric Distribution -- Geometric and Negative Binomial Distributions -- Poisson Distribution -- 2.4 Continuous Random Variables -- 2.4.1 Absolute Continuity of Measures -- 2.4.2 Densities -- Properties of Densities of Random Variables -- 2.4.3 Classical Distributions with Densities -- Uniform Distribution -- Gaussian Distribution -- Gamma, Exponential, Chi-Squared, Erlang Distributions -- Log-Normal Distribution -- Weibull Distribution -- Inverse-Gaussian Distribution -- Beta Distribution -- Fisher Distribution -- Student and Cauchy Distributions -- 2.4.4 Determination of Distributions -- 2.5 Analytical Tools -- 2.5.1 Generating Functions -- Properties of Generating Functions -- 2.5.2 Fourier Transform and Characteristic Functions -- Properties of Characteristic Functions -- 2.5.3 Laplace Transform.
Properties of Laplace Transforms -- 2.5.4 Moment Generating Functions and Cramér Transform -- Properties of Cramér Transform -- 2.6 Reliability and Survival Analysis -- 2.7 Exercises and Complements -- 3 Random Vectors -- 3.1 Relations Between Random Variables -- 3.1.1 Covariance -- Properties of Covariance and Correlation Coefficients -- 3.1.2 Independence of Random Variables -- 3.1.3 Stochastic Order Relation -- 3.1.4 Entropy -- Properties of Entropy -- 3.2 Characteristics of Random Vectors -- 3.2.1 Product of Probability Spaces -- 3.2.2 Distribution of Random Vectors -- Properties of Multi-dimensional Distribution Functions -- Properties of Densities of Random Vectors -- Properties of Covariance Matrices -- 3.2.3 Independence of Random Vectors -- Properties of Covariance Matrices of Two Vectors -- 3.3 Functions of Random Vectors -- 3.3.1 Order Statistics -- 3.3.2 Sums of Independent Variables or Vectors -- Properties of Convolution -- 3.3.3 Determination of Distributions -- 3.4 Gaussian Vectors -- 3.5 Exercises and Complements -- 4 Random Sequences -- 4.1 Enumerable Sequences -- 4.1.1 Sequences of Events -- Properties of Superior and Inferior Limits of Events -- 4.1.2 Independence of Sequences -- 4.2 Stochastic Convergence -- 4.2.1 Different Types of Convergence -- 4.2.2 Convergence Criteria -- 4.2.3 Links Between Convergences -- 4.2.4 Convergence of Sequences of Random Vectors -- 4.3 Limit Theorems -- 4.3.1 Asymptotics of Discrete Distributions -- 4.3.2 Laws of Large Numbers -- 4.3.3 Central Limit Theorem -- 4.4 Stochastic Simulation Methods -- 4.4.1 Generating Random Variables -- 4.4.2 Monte Carlo Simulation Method -- 4.5 Exercises and Complements -- 5 Introduction to Statistics -- 5.1 Non-parametric Statistics -- 5.1.1 Empirical Distribution Function -- 5.1.2 Confidence Intervals -- 5.1.3 Non-parametric Testing -- 5.2 Parametric Statistics.
5.2.1 Point Estimation -- 5.2.2 Maximum Likelihood Method -- 5.2.3 Precision of the Estimators -- 5.2.4 Parametric Confidence Intervals -- 5.2.5 Testing in a Parametric Model -- 5.3 The Linear Model -- 5.3.1 Linear and Quadratic Approximations -- 5.3.2 The Simple Linear Model -- 5.3.3 ANOVA -- For Two Samples -- One Way Model -- Two Way Model -- 5.4 Exercises and Complements -- Further Reading -- Measure and Probability -- Probability Theory and Statistics -- Applications -- Index.
Record Nr. UNISA-996479370903316
Girardin Valérie  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Applied statistical methods : ISGES 2020, Pune, India, January 2-4 / / edited by David D. Hanagal, Raosaheb V. Latpate, and Girish Chandra
Applied statistical methods : ISGES 2020, Pune, India, January 2-4 / / edited by David D. Hanagal, Raosaheb V. Latpate, and Girish Chandra
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (318 pages)
Disciplina 519.54
Collana Springer Proceedings in Mathematics and Statistics
Soggetto topico Mathematical statistics
Estadística matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 981-16-7931-2
981-16-7932-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Obituary -- Contents -- Editors and Contributors -- Bayesian Order-Restricted Inference of Multinomial Counts from Small Areas -- 1 Introduction -- 2 Multinomial Dirichlet Models -- 2.1 Model Without Order Restriction (M1) -- 2.2 Model with Order Restrictions (M2) -- 3 Computations -- 3.1 Sampling θ in M2 -- 3.2 Gibbs Sampling for µ and τ -- 4 Application to BMI -- 4.1 Body Mass Index -- 4.2 MCMC Convergence -- 4.3 Model Comparison -- 5 Bayesian Diagnostics -- 6 Conclusion -- 7 Appendix -- 7.1 Details of Gibbs Sampling for µ and τ -- 7.2 Model Comparison -- References -- A Hierarchical Bayesian Beta-Binomial Model for Sub-areas -- 1 Introduction -- 2 Hierarchical Bayesian Small Area Models -- 2.1 A One-Fold Beta-Binomial Model -- 2.2 A Two-Fold Beta-Binomial Model -- 3 Computation -- 3.1 Approximation Method -- 3.2 Exact Method -- 4 Numerical Example -- 4.1 Nepal Living Standards Survey II -- 4.2 Numerical Comparison -- 5 Conclusion and Future Work -- Appendix A Some Details about Approximation of π(µi |τ) -- Appendix B Propriety of the One-Fold Model -- References -- Hierarchical Bayes Inference from Survey-Weighted Small Domain Proportions -- 1 Introduction -- 2 Hierarchical Bayesian Framework -- 3 Application -- 4 Concluding Remarks -- References -- Efficiency of Ranked Set Sampling Design in Goodness of Fit Tests for Cauchy Distribution -- 1 Introduction -- 2 Goodness of Fit Test for Cauchy Distribution -- 3 Power Comparison -- 4 Conclusion -- References -- Fuzzy Supply Chain Newsboy Problem Under Lognormal Distributed Demand for Bakery Products -- 1 Introduction -- 2 Preliminary Definitions and Mathematical Model -- 2.1 Notations and Assumptions -- 2.2 Mathematical Model -- 3 Numerical Case Study -- 4 Results and Discussion -- 5 Conclusions -- References.
Probabilistic Supply Chain Models with Partial Backlogging for Deteriorating Items -- 1 Introduction -- 2 Mathematical Model -- 2.1 Preliminaries -- 3 Genetic Algorithm -- 4 Numerical Example -- 5 Sensitivity Analysis -- 6 Managerial Implications -- 7 Conclusions -- References -- The Evolution of Dynamic Gaussian Process Model with Applications to Malaria Vaccine Coverage Prediction -- 1 Introduction -- 2 Evolution of Dynamic Gaussian Process Model -- 2.1 Basic Gaussian Process Model -- 2.2 Dynamic Gaussian Process Model -- 2.3 Generalizations for Big Data -- 3 Application: Malaria Vaccination Coverage -- 4 Concluding Remarks -- References -- Grey Relational Analysis for the Selection of Potential Isolates of Alternaria Alternata of Poplar -- 1 Introduction -- 2 Material and Methods -- 2.1 Survey and Collection of Alternaria Isolates -- 2.2 GRA for Selection of Potent Isolates -- 3 Results -- 3.1 Growth Attributes of A. Alternata -- 3.2 Grey Relational Generating, Coefficients and Grades -- 3.3 Performance Evaluation of Selected Isolates -- 4 Discussion and Conclusion -- References -- Decision Making for Multi-Items Inventory Models -- 1 Introduction -- 2 Notations and Assumptions -- 2.1 Assumptions -- 2.2 Notations -- 3 Mathematical Model -- 4 Numerical Example and Comparison Study -- 5 Conclusion -- References -- Modeling Australian Twin Data Using Generalized Lindley Shared Frailty Models -- 1 Introduction -- 2 Reversed Hazard Rate -- 3 General Shared Frailty Model -- 4 Generalized Lindley Frailty Model -- 5 Dependence Measure -- 6 Baseline Distributions -- 6.1 Modified Inverse Weibull Distribution -- 6.2 Generalized Rayleigh Distribution -- 7 Proposed Models -- 8 Statistical Properties -- 8.1 Bivariate Density Function -- 8.2 Bivariate Survival Function -- 8.3 Hazard Gradient Function -- 8.4 Conditional Probability Measure.
8.5 Cross-ratio Function -- 9 Likelihood Design and Bayesian Paradigm -- 10 Simulation Study -- 11 Analysis of Australian Twin Data -- 12 Conclusions -- References -- Ultimate Ruin Probability for Benktander Gibrat Risk Model -- 1 Introduction -- 2 Risk Model -- 3 Laplace Transformation -- 4 Ultimate Ruin Probability for BG Distribution -- 5 Calculation of Ultimate Ruin Probability -- 6 Conclusions -- References -- Test of Homogeneity of Scale Parameters Based on Function of Sample Quasi Ranges -- 1 Introduction -- 2 Proposed Test Procedure -- 3 Calculation of Critical Points for Some Specific Distributions: Simulation Method -- 3.1 Critical Points for Standard Exponential, Standard Logistic and Standard Uniform Distributions -- 4 Simultaneous One-Sided Confidence Intervals (SOCI's) of the Proposed Test -- 4.1 Simulated Example to Compute Test Statistic and SOCIs -- 5 Power of the Proposed Test -- 6 Conclusion -- References -- A Bayesian Response-Adaptive, Covariate-Balanced and Q-Learning-Decision-Consistent Randomization Method for SMART Designs -- 1 Introduction -- 2 Methods -- 2.1 Overview of the SMART Design -- 2.2 Randomization Probability Using Q-Learning-Based Optimal Decisions -- 2.3 Covariate-Balanced Randomization Probability According to the Prognostic Score for SMART Designs -- 2.4 Response-Adaptive Randomization Probability Based on Outcomes of Previous Groups -- 2.5 Response-Adaptive, Covariate-Balanced and Q-Learning-Decision-Consistent (RCQ) Randomization Method -- 3 Simulations Models and Assessment Measures -- 3.1 Simulation Models -- 3.2 Assessment Measures -- 3.3 Simulation Results -- 4 Discussion -- References -- An Introduction to Bayesian Inference for Finite Population Characteristics -- 1 Introduction -- 2 Normal Distribution -- 3 Regression -- 4 Dirichlet Process -- 5 Multiple Regression with Post-stratification.
6 Categorical Data -- 7 Summary and Discussion -- References -- Reliability Measures of Repairable Systems with Arrival Time of Server -- 1 Introduction -- 2 Literature Review -- 3 Some Fundamentals -- 3.1 Reliability -- 3.2 Mean Time to System Failure (MTSF) -- 3.3 Steady-State Availability -- 3.4 Redundancy -- 3.5 Semi-Markov Process -- 3.6 Regenerative Point Process -- 4 Common Notations -- 5 Reliability Measures of Repairable Systems -- 5.1 MTSF and Availability of a Single Unit System with Arrival Time of the Server -- 5.2 MTSF and Availability of a Two-Unit Cold Standby System with Arrival Time of the Server -- 5.3 MTSF and Availability of a Two-Unit Parallel System with Arrival Time of the Server -- 6 Discussion and Conclusion -- References -- Stress-strength Reliability Estimation for Multi-component System Based on Upper Record Values Under New Weibull-Pareto Distribution -- 1 Introduction -- 2 System Reliability -- 3 Maximum Likelihood Estimators (MLE) of Parameters -- 4 Likelihood Ratio (LR) Test for Equality of Scale Parameters -- 5 Estimation of Rs,k Using Maximum Likelihood and Bayesian Methods -- 6 Simulation Study -- 7 Real Data Analysis -- 8 Summary and Conclusions -- References -- Record Values and Associated Inference on Muth Distribution -- 1 Introduction -- 2 Survival Function, Joint and Conditional Densities, and Moments of Upper Records from Muth Distribution -- 3 Parameter Estimation Based on Upper Records Using Moment, Likelihood, and Bayesian Approaches -- 3.1 Moment Estimation of α -- 3.2 Maximum Likelihood Estimation -- 3.3 Bayesian Estimation -- 4 Numerical Illustration -- 5 Real-life Application -- 6 Prediction of Future Records -- 6.1 Frequentist Approach -- 6.2 Bayesian Approach -- 7 Concluding Remarks -- References -- Statistical Linear Calibration in Data with Measurement Errors -- 1 Introduction.
2 Development of Calibration Estimators -- 3 Performance Properties -- 3.1 Large Sample Asymptotic Bias (LSAB) -- 3.2 Large Sample Asymptotic Variance (LSAV) -- 4 An Example -- 5 Conclusions -- References.
Record Nr. UNISA-996549467303316
Gateway East, Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Applied statistical methods : ISGES 2020, Pune, India, January 2-4 / / edited by David D. Hanagal, Raosaheb V. Latpate, and Girish Chandra
Applied statistical methods : ISGES 2020, Pune, India, January 2-4 / / edited by David D. Hanagal, Raosaheb V. Latpate, and Girish Chandra
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (318 pages)
Disciplina 519.54
Collana Springer Proceedings in Mathematics and Statistics
Soggetto topico Mathematical statistics
Estadística matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 981-16-7931-2
981-16-7932-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Obituary -- Contents -- Editors and Contributors -- Bayesian Order-Restricted Inference of Multinomial Counts from Small Areas -- 1 Introduction -- 2 Multinomial Dirichlet Models -- 2.1 Model Without Order Restriction (M1) -- 2.2 Model with Order Restrictions (M2) -- 3 Computations -- 3.1 Sampling θ in M2 -- 3.2 Gibbs Sampling for µ and τ -- 4 Application to BMI -- 4.1 Body Mass Index -- 4.2 MCMC Convergence -- 4.3 Model Comparison -- 5 Bayesian Diagnostics -- 6 Conclusion -- 7 Appendix -- 7.1 Details of Gibbs Sampling for µ and τ -- 7.2 Model Comparison -- References -- A Hierarchical Bayesian Beta-Binomial Model for Sub-areas -- 1 Introduction -- 2 Hierarchical Bayesian Small Area Models -- 2.1 A One-Fold Beta-Binomial Model -- 2.2 A Two-Fold Beta-Binomial Model -- 3 Computation -- 3.1 Approximation Method -- 3.2 Exact Method -- 4 Numerical Example -- 4.1 Nepal Living Standards Survey II -- 4.2 Numerical Comparison -- 5 Conclusion and Future Work -- Appendix A Some Details about Approximation of π(µi |τ) -- Appendix B Propriety of the One-Fold Model -- References -- Hierarchical Bayes Inference from Survey-Weighted Small Domain Proportions -- 1 Introduction -- 2 Hierarchical Bayesian Framework -- 3 Application -- 4 Concluding Remarks -- References -- Efficiency of Ranked Set Sampling Design in Goodness of Fit Tests for Cauchy Distribution -- 1 Introduction -- 2 Goodness of Fit Test for Cauchy Distribution -- 3 Power Comparison -- 4 Conclusion -- References -- Fuzzy Supply Chain Newsboy Problem Under Lognormal Distributed Demand for Bakery Products -- 1 Introduction -- 2 Preliminary Definitions and Mathematical Model -- 2.1 Notations and Assumptions -- 2.2 Mathematical Model -- 3 Numerical Case Study -- 4 Results and Discussion -- 5 Conclusions -- References.
Probabilistic Supply Chain Models with Partial Backlogging for Deteriorating Items -- 1 Introduction -- 2 Mathematical Model -- 2.1 Preliminaries -- 3 Genetic Algorithm -- 4 Numerical Example -- 5 Sensitivity Analysis -- 6 Managerial Implications -- 7 Conclusions -- References -- The Evolution of Dynamic Gaussian Process Model with Applications to Malaria Vaccine Coverage Prediction -- 1 Introduction -- 2 Evolution of Dynamic Gaussian Process Model -- 2.1 Basic Gaussian Process Model -- 2.2 Dynamic Gaussian Process Model -- 2.3 Generalizations for Big Data -- 3 Application: Malaria Vaccination Coverage -- 4 Concluding Remarks -- References -- Grey Relational Analysis for the Selection of Potential Isolates of Alternaria Alternata of Poplar -- 1 Introduction -- 2 Material and Methods -- 2.1 Survey and Collection of Alternaria Isolates -- 2.2 GRA for Selection of Potent Isolates -- 3 Results -- 3.1 Growth Attributes of A. Alternata -- 3.2 Grey Relational Generating, Coefficients and Grades -- 3.3 Performance Evaluation of Selected Isolates -- 4 Discussion and Conclusion -- References -- Decision Making for Multi-Items Inventory Models -- 1 Introduction -- 2 Notations and Assumptions -- 2.1 Assumptions -- 2.2 Notations -- 3 Mathematical Model -- 4 Numerical Example and Comparison Study -- 5 Conclusion -- References -- Modeling Australian Twin Data Using Generalized Lindley Shared Frailty Models -- 1 Introduction -- 2 Reversed Hazard Rate -- 3 General Shared Frailty Model -- 4 Generalized Lindley Frailty Model -- 5 Dependence Measure -- 6 Baseline Distributions -- 6.1 Modified Inverse Weibull Distribution -- 6.2 Generalized Rayleigh Distribution -- 7 Proposed Models -- 8 Statistical Properties -- 8.1 Bivariate Density Function -- 8.2 Bivariate Survival Function -- 8.3 Hazard Gradient Function -- 8.4 Conditional Probability Measure.
8.5 Cross-ratio Function -- 9 Likelihood Design and Bayesian Paradigm -- 10 Simulation Study -- 11 Analysis of Australian Twin Data -- 12 Conclusions -- References -- Ultimate Ruin Probability for Benktander Gibrat Risk Model -- 1 Introduction -- 2 Risk Model -- 3 Laplace Transformation -- 4 Ultimate Ruin Probability for BG Distribution -- 5 Calculation of Ultimate Ruin Probability -- 6 Conclusions -- References -- Test of Homogeneity of Scale Parameters Based on Function of Sample Quasi Ranges -- 1 Introduction -- 2 Proposed Test Procedure -- 3 Calculation of Critical Points for Some Specific Distributions: Simulation Method -- 3.1 Critical Points for Standard Exponential, Standard Logistic and Standard Uniform Distributions -- 4 Simultaneous One-Sided Confidence Intervals (SOCI's) of the Proposed Test -- 4.1 Simulated Example to Compute Test Statistic and SOCIs -- 5 Power of the Proposed Test -- 6 Conclusion -- References -- A Bayesian Response-Adaptive, Covariate-Balanced and Q-Learning-Decision-Consistent Randomization Method for SMART Designs -- 1 Introduction -- 2 Methods -- 2.1 Overview of the SMART Design -- 2.2 Randomization Probability Using Q-Learning-Based Optimal Decisions -- 2.3 Covariate-Balanced Randomization Probability According to the Prognostic Score for SMART Designs -- 2.4 Response-Adaptive Randomization Probability Based on Outcomes of Previous Groups -- 2.5 Response-Adaptive, Covariate-Balanced and Q-Learning-Decision-Consistent (RCQ) Randomization Method -- 3 Simulations Models and Assessment Measures -- 3.1 Simulation Models -- 3.2 Assessment Measures -- 3.3 Simulation Results -- 4 Discussion -- References -- An Introduction to Bayesian Inference for Finite Population Characteristics -- 1 Introduction -- 2 Normal Distribution -- 3 Regression -- 4 Dirichlet Process -- 5 Multiple Regression with Post-stratification.
6 Categorical Data -- 7 Summary and Discussion -- References -- Reliability Measures of Repairable Systems with Arrival Time of Server -- 1 Introduction -- 2 Literature Review -- 3 Some Fundamentals -- 3.1 Reliability -- 3.2 Mean Time to System Failure (MTSF) -- 3.3 Steady-State Availability -- 3.4 Redundancy -- 3.5 Semi-Markov Process -- 3.6 Regenerative Point Process -- 4 Common Notations -- 5 Reliability Measures of Repairable Systems -- 5.1 MTSF and Availability of a Single Unit System with Arrival Time of the Server -- 5.2 MTSF and Availability of a Two-Unit Cold Standby System with Arrival Time of the Server -- 5.3 MTSF and Availability of a Two-Unit Parallel System with Arrival Time of the Server -- 6 Discussion and Conclusion -- References -- Stress-strength Reliability Estimation for Multi-component System Based on Upper Record Values Under New Weibull-Pareto Distribution -- 1 Introduction -- 2 System Reliability -- 3 Maximum Likelihood Estimators (MLE) of Parameters -- 4 Likelihood Ratio (LR) Test for Equality of Scale Parameters -- 5 Estimation of Rs,k Using Maximum Likelihood and Bayesian Methods -- 6 Simulation Study -- 7 Real Data Analysis -- 8 Summary and Conclusions -- References -- Record Values and Associated Inference on Muth Distribution -- 1 Introduction -- 2 Survival Function, Joint and Conditional Densities, and Moments of Upper Records from Muth Distribution -- 3 Parameter Estimation Based on Upper Records Using Moment, Likelihood, and Bayesian Approaches -- 3.1 Moment Estimation of α -- 3.2 Maximum Likelihood Estimation -- 3.3 Bayesian Estimation -- 4 Numerical Illustration -- 5 Real-life Application -- 6 Prediction of Future Records -- 6.1 Frequentist Approach -- 6.2 Bayesian Approach -- 7 Concluding Remarks -- References -- Statistical Linear Calibration in Data with Measurement Errors -- 1 Introduction.
2 Development of Calibration Estimators -- 3 Performance Properties -- 3.1 Large Sample Asymptotic Bias (LSAB) -- 3.2 Large Sample Asymptotic Variance (LSAV) -- 4 An Example -- 5 Conclusions -- References.
Record Nr. UNINA-9910743343603321
Gateway East, Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Applied statistics and data science : proceedings of Statistics 2021 Canada, selected contributions / / edited by Yogendra P. Chaubey [and three others]
Applied statistics and data science : proceedings of Statistics 2021 Canada, selected contributions / / edited by Yogendra P. Chaubey [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (163 pages)
Disciplina 519.5
Collana Springer Proceedings in Mathematics and Statistics
Soggetto topico Mathematical statistics
Estadística matemàtica
Congressos
Soggetto genere / forma Llibres electrònics
ISBN 3-030-86133-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996466563403316
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Applied statistics and data science : proceedings of Statistics 2021 Canada, selected contributions / / edited by Yogendra P. Chaubey [and three others]
Applied statistics and data science : proceedings of Statistics 2021 Canada, selected contributions / / edited by Yogendra P. Chaubey [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2021]
Descrizione fisica 1 online resource (163 pages)
Disciplina 519.5
Collana Springer Proceedings in Mathematics and Statistics
Soggetto topico Mathematical statistics
Estadística matemàtica
Congressos
Soggetto genere / forma Llibres electrònics
ISBN 3-030-86133-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910520068603321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
Autore Lash Timothy L.
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (475 pages)
Disciplina 614.4072
Collana Statistics for Biology and Health
Soggetto topico Epidemiology - Research
Social sciences - Methodology
Epidemiologia
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-82673-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction, Objectives, and an Alternative -- Introduction: Biases in Health Research -- Statistical Inference in Public Health Research -- The Treatment of Uncertainty in Nonrandomized Research -- When Bias Analysis Will Be Most Useful -- Judgments Under Uncertainty -- The Dual-Process Model of Cognition -- Anchoring and Adjustment -- Overconfidence -- Failure to Account for the Base-Rate -- Conclusion -- References -- Chapter 2: A Guide to Implementing Quantitative Bias Analysis -- Introduction -- Reducing Error -- Reducing Error by Design -- Reducing Error in the Analysis -- Quantifying Error -- Evaluating the Potential Value of Quantitative Bias Analysis? -- Planning for Bias Analysis -- Creating a Data Collection Plan for Bias Analysis -- Creating an Analytic Plan for a Bias Analysis -- Type of Data: Record-Level Versus Summary -- Type of Bias Analysis -- Order of Bias Analysis Adjustments -- Bias Analysis Techniques -- Simple Bias Analysis -- Multidimensional Bias Analysis -- Probabilistic Bias Analysis -- Multiple Bias Modeling -- Direct Bias Modeling and Missing Data Methods -- Bayesian Bias Analysis -- Assigning Values and Distributions to Bias Parameters -- Directed Acyclic Graphs -- Conclusion -- References -- Chapter 3: Data Sources for Bias Analysis -- Bias Parameters -- Internal Data Sources -- Selection Bias -- Uncontrolled Confounding -- Information Bias -- Design of Internal Validation Studies -- Limitations of Internal Validation Studies -- External Data Sources -- Selection Bias -- Unmeasured Confounder -- Information Bias -- Expert Opinion -- Summary -- References -- Chapter 4: Selection Bias -- Introduction -- Definitions and Terms -- Conceptual -- Depicting Selection Bias Using Causal Graphs -- Design Considerations -- Bias Analysis.
Motivation for Bias Analysis -- Sources of Data -- Simple Bias-Adjustment for Differential Initial Participation -- Example -- Introduction to Bias Analysis -- Bias Analysis by Projecting the Exposed Proportion Among Nonparticipants -- Bias Analysis Using Selection Proportions -- Bias Analysis Using Inverse Probability of Participation Weighting -- Simple Bias-Adjustment for Differential Loss-to-Follow-up -- Example -- Bias Analysis by Modeling Outcomes -- Bias Analysis by Inverse Probability of Attrition Weighting -- Multidimensional Bias Analysis for Selection Bias -- Example -- References -- Chapter 5: Uncontrolled Confounders -- Introduction -- Key Concepts -- Definitions -- Motivation for Bias Analysis -- Data Sources -- Introduction to Simple Bias Analysis -- Approach -- Introduction to the Example -- Bias Parameters -- Implementation of Simple Bias Analysis -- Ratio Measures -- Example -- Difference Measures -- Person-time Designs -- Unmeasured Confounder in the Presence of Effect Measure Modification -- Polytomous Confounders -- Multidimensional Bias Analysis for Unmeasured Confounding -- Example -- Bounding the Bias Limits of an Unmeasured Confounding -- Analytic Approach -- The E-Value and G-Value -- Signed Directed Acyclic Graphs to Estimate the Direction of Bias -- References -- Chapter 6: Misclassification -- Introduction -- Definitions and Terms -- Differential vs. Nondifferential Misclassification -- Dependent vs. Independent Misclassification -- Directed Acyclic Graphs and Misclassification -- Calculating Classification Bias Parameters from Validation Data -- Sources of Data -- Bias Analysis of Exposure Misclassification -- Bias-Adjusting for Exposure Misclassification Using Sensitivity and Specificity: Nondifferential and Independent Errors -- Bias-Adjusting for Exposure Misclassification Using Predictive Values.
Bias-Adjustment for Nondifferential Outcome Misclassification Using Positive Predictive Values for the Risk Ratio Measure of A... -- Bias-Adjustments Using Sensitivity and Specificity: Differential Independent Errors -- Bias-Adjustments Using Sensitivity and Specificity: Internal Validation Data -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Disease Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Independent Errors -- Disease Misclassification in Case-Control Studies -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Covariate Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Differential Misclassification with Independent Errors -- Overreliance on Nondifferential Misclassification of Covariates Biasing Toward the Null -- Dependent Misclassification -- Matrix Method for Misclassification Adjustment -- Multidimensional Bias Analysis for Misclassification -- Limitations -- Negative Expected Cell Frequencies -- Other Considerations -- References -- Chapter 7: Preparing for Probabilistic Bias Analysis -- Introduction -- Preparing for Probabilistic Bias Analysis -- Statistical Software for Probabilistic Bias Analysis -- Summary Level Versus Record Level Probabilistic Bias Analysis -- Describing Uncertainty in the Bias Parameters -- Probability Distributions -- Uniform Distribution -- Generalized Method for Sampling from Distributions -- Trapezoidal Distribution -- Triangular Distribution -- Normal Distribution -- Beta Distribution -- Bernoulli and Binomial Distributions -- Other Probability Distributions -- Sensitivity to Chosen Distributions -- Correlated Distributions -- Conclusions -- References -- Chapter 8: Probabilistic Bias Analysis for Simulation of Summary Level Data -- Introduction.
Analytic Approach for Summary Level Probabilistic Bias Analysis -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Incorporate Conventional Random Error by Sampling Summary Statistics -- Step 4c (Alternate): Resample the Prevalence of Misclassification Adjusted Exposure -- Step 5: Save the Bias-Adjusted Estimate and Repeat Steps 4a-c -- Step 6: Summarize the Bias-Adjusted Estimates with a Frequency Distribution that Yields a Central Tendency and Simulation Inte... -- Misclassification Implementation: Predictive Values -- Misclassification Implementation: Predictive Values - Alternative -- Misclassification of Outcomes and Confounders -- Uncontrolled Confounding Implementation -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Confounding Implementation Alternative: Relative Risk Due to Confounding -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions.
Step 4b: Generate Bias-Adjusted Results Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Implementation -- An Example of Probabilistic Bias Analysis in the Presence of Substantial Source Population Data -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Adjustment Using Selection Probabilities -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Computing Issues with Summary Level Probabilistic Bias Analysis -- Bootstrapping -- Impossible Values for Bias Parameters and Model Diagnostic Plots -- Conclusions -- Appendix: Sampling Models for Exposure Misclassification -- References -- Chapter 9: Probabilistic Bias Analysis for Simulation of Record-Level Data -- Introduction -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters.
Step 3: Assign Probability Distributions to Each Bias Parameter.
Record Nr. UNINA-9910556891103321
Lash Timothy L.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
Autore Lash Timothy L.
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (475 pages)
Disciplina 614.4072
Collana Statistics for Biology and Health
Soggetto topico Epidemiology - Research
Social sciences - Methodology
Epidemiologia
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-82673-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction, Objectives, and an Alternative -- Introduction: Biases in Health Research -- Statistical Inference in Public Health Research -- The Treatment of Uncertainty in Nonrandomized Research -- When Bias Analysis Will Be Most Useful -- Judgments Under Uncertainty -- The Dual-Process Model of Cognition -- Anchoring and Adjustment -- Overconfidence -- Failure to Account for the Base-Rate -- Conclusion -- References -- Chapter 2: A Guide to Implementing Quantitative Bias Analysis -- Introduction -- Reducing Error -- Reducing Error by Design -- Reducing Error in the Analysis -- Quantifying Error -- Evaluating the Potential Value of Quantitative Bias Analysis? -- Planning for Bias Analysis -- Creating a Data Collection Plan for Bias Analysis -- Creating an Analytic Plan for a Bias Analysis -- Type of Data: Record-Level Versus Summary -- Type of Bias Analysis -- Order of Bias Analysis Adjustments -- Bias Analysis Techniques -- Simple Bias Analysis -- Multidimensional Bias Analysis -- Probabilistic Bias Analysis -- Multiple Bias Modeling -- Direct Bias Modeling and Missing Data Methods -- Bayesian Bias Analysis -- Assigning Values and Distributions to Bias Parameters -- Directed Acyclic Graphs -- Conclusion -- References -- Chapter 3: Data Sources for Bias Analysis -- Bias Parameters -- Internal Data Sources -- Selection Bias -- Uncontrolled Confounding -- Information Bias -- Design of Internal Validation Studies -- Limitations of Internal Validation Studies -- External Data Sources -- Selection Bias -- Unmeasured Confounder -- Information Bias -- Expert Opinion -- Summary -- References -- Chapter 4: Selection Bias -- Introduction -- Definitions and Terms -- Conceptual -- Depicting Selection Bias Using Causal Graphs -- Design Considerations -- Bias Analysis.
Motivation for Bias Analysis -- Sources of Data -- Simple Bias-Adjustment for Differential Initial Participation -- Example -- Introduction to Bias Analysis -- Bias Analysis by Projecting the Exposed Proportion Among Nonparticipants -- Bias Analysis Using Selection Proportions -- Bias Analysis Using Inverse Probability of Participation Weighting -- Simple Bias-Adjustment for Differential Loss-to-Follow-up -- Example -- Bias Analysis by Modeling Outcomes -- Bias Analysis by Inverse Probability of Attrition Weighting -- Multidimensional Bias Analysis for Selection Bias -- Example -- References -- Chapter 5: Uncontrolled Confounders -- Introduction -- Key Concepts -- Definitions -- Motivation for Bias Analysis -- Data Sources -- Introduction to Simple Bias Analysis -- Approach -- Introduction to the Example -- Bias Parameters -- Implementation of Simple Bias Analysis -- Ratio Measures -- Example -- Difference Measures -- Person-time Designs -- Unmeasured Confounder in the Presence of Effect Measure Modification -- Polytomous Confounders -- Multidimensional Bias Analysis for Unmeasured Confounding -- Example -- Bounding the Bias Limits of an Unmeasured Confounding -- Analytic Approach -- The E-Value and G-Value -- Signed Directed Acyclic Graphs to Estimate the Direction of Bias -- References -- Chapter 6: Misclassification -- Introduction -- Definitions and Terms -- Differential vs. Nondifferential Misclassification -- Dependent vs. Independent Misclassification -- Directed Acyclic Graphs and Misclassification -- Calculating Classification Bias Parameters from Validation Data -- Sources of Data -- Bias Analysis of Exposure Misclassification -- Bias-Adjusting for Exposure Misclassification Using Sensitivity and Specificity: Nondifferential and Independent Errors -- Bias-Adjusting for Exposure Misclassification Using Predictive Values.
Bias-Adjustment for Nondifferential Outcome Misclassification Using Positive Predictive Values for the Risk Ratio Measure of A... -- Bias-Adjustments Using Sensitivity and Specificity: Differential Independent Errors -- Bias-Adjustments Using Sensitivity and Specificity: Internal Validation Data -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Disease Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Independent Errors -- Disease Misclassification in Case-Control Studies -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Covariate Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Differential Misclassification with Independent Errors -- Overreliance on Nondifferential Misclassification of Covariates Biasing Toward the Null -- Dependent Misclassification -- Matrix Method for Misclassification Adjustment -- Multidimensional Bias Analysis for Misclassification -- Limitations -- Negative Expected Cell Frequencies -- Other Considerations -- References -- Chapter 7: Preparing for Probabilistic Bias Analysis -- Introduction -- Preparing for Probabilistic Bias Analysis -- Statistical Software for Probabilistic Bias Analysis -- Summary Level Versus Record Level Probabilistic Bias Analysis -- Describing Uncertainty in the Bias Parameters -- Probability Distributions -- Uniform Distribution -- Generalized Method for Sampling from Distributions -- Trapezoidal Distribution -- Triangular Distribution -- Normal Distribution -- Beta Distribution -- Bernoulli and Binomial Distributions -- Other Probability Distributions -- Sensitivity to Chosen Distributions -- Correlated Distributions -- Conclusions -- References -- Chapter 8: Probabilistic Bias Analysis for Simulation of Summary Level Data -- Introduction.
Analytic Approach for Summary Level Probabilistic Bias Analysis -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Incorporate Conventional Random Error by Sampling Summary Statistics -- Step 4c (Alternate): Resample the Prevalence of Misclassification Adjusted Exposure -- Step 5: Save the Bias-Adjusted Estimate and Repeat Steps 4a-c -- Step 6: Summarize the Bias-Adjusted Estimates with a Frequency Distribution that Yields a Central Tendency and Simulation Inte... -- Misclassification Implementation: Predictive Values -- Misclassification Implementation: Predictive Values - Alternative -- Misclassification of Outcomes and Confounders -- Uncontrolled Confounding Implementation -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Confounding Implementation Alternative: Relative Risk Due to Confounding -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions.
Step 4b: Generate Bias-Adjusted Results Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Implementation -- An Example of Probabilistic Bias Analysis in the Presence of Substantial Source Population Data -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Adjustment Using Selection Probabilities -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Computing Issues with Summary Level Probabilistic Bias Analysis -- Bootstrapping -- Impossible Values for Bias Parameters and Model Diagnostic Plots -- Conclusions -- Appendix: Sampling Models for Exposure Misclassification -- References -- Chapter 9: Probabilistic Bias Analysis for Simulation of Record-Level Data -- Introduction -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters.
Step 3: Assign Probability Distributions to Each Bias Parameter.
Record Nr. UNISA-996466557403316
Lash Timothy L.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui