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Biblioteca

MARC Lista (tabellare)
Advances in Statistics - Theory and Applications [[electronic resource] ] : Honoring the Contributions of Barry C. Arnold in Statistical Science / / edited by Indranil Ghosh, N. Balakrishnan, Hon Keung Tony Ng
Advances in Statistics - Theory and Applications [[electronic resource] ] : Honoring the Contributions of Barry C. Arnold in Statistical Science / / edited by Indranil Ghosh, N. Balakrishnan, Hon Keung Tony Ng
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (xxxviii, 421 pages)
Disciplina 519.5
Collana Emerging Topics in Statistics and Biostatistics
Soggetto topico Statistics
Biometry
Probabilities
Statistical Theory and Methods
Biostatistics
Probability Theory
Bayesian Inference
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-62900-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Wrapped gamma distribution for modeling and inference with asymmetric circular data - Ashis SenGupta, Carlos A. Coelho, Choung Min Ng -- 2. Goodness of fit tests for Cauchy distributions using data transformations - Jose A. Villasenor -- 3. A note on the product of independent beta random variables - Filipe J. Marques, Indranil Ghosh, Johan Ferreira, Andriette Bekker -- 4. Properties of system lifetime distributions in the classic model of independent exponential component lifetimes - Tomasz Rychlik -- 5. On the exact statistical distribution of econometric estimators and test statistics - Yong Bao, Xiaotian Liu, Aman Ullah -- 6. On conditional tail inferences from multivariate distributions - Harry Joe -- 7. A bivariate distribution with generalized exponential conditionals: Theory and applications - Miroslav Ristic, Bozidar V. Popovic, Indranil Ghosh -- 8. Assessment of distributional goodness-of-fit for modeling the superposition of renewal process data - Wei Zhang, William Q. Meeker -- 9. Skew-Elliptical Thomas point processes - Ngoc Anh Dao, Marc G. Genton -- 10. Bayesian model assessment and selection using Bregman divergence - Gyuhyeong Goh, Dipak K. Dey -- 11. On hidden truncation in non-normal models - Indranil Ghosh, Hon Keung Tony Ng.
Record Nr. UNINA-9910483453003321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in Statistics - Theory and Applications [[electronic resource] ] : Honoring the Contributions of Barry C. Arnold in Statistical Science / / edited by Indranil Ghosh, N. Balakrishnan, Hon Keung Tony Ng
Advances in Statistics - Theory and Applications [[electronic resource] ] : Honoring the Contributions of Barry C. Arnold in Statistical Science / / edited by Indranil Ghosh, N. Balakrishnan, Hon Keung Tony Ng
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (xxxviii, 421 pages)
Disciplina 519.5
Collana Emerging Topics in Statistics and Biostatistics
Soggetto topico Statistics
Biometry
Probabilities
Statistical Theory and Methods
Biostatistics
Probability Theory
Bayesian Inference
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-62900-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Wrapped gamma distribution for modeling and inference with asymmetric circular data - Ashis SenGupta, Carlos A. Coelho, Choung Min Ng -- 2. Goodness of fit tests for Cauchy distributions using data transformations - Jose A. Villasenor -- 3. A note on the product of independent beta random variables - Filipe J. Marques, Indranil Ghosh, Johan Ferreira, Andriette Bekker -- 4. Properties of system lifetime distributions in the classic model of independent exponential component lifetimes - Tomasz Rychlik -- 5. On the exact statistical distribution of econometric estimators and test statistics - Yong Bao, Xiaotian Liu, Aman Ullah -- 6. On conditional tail inferences from multivariate distributions - Harry Joe -- 7. A bivariate distribution with generalized exponential conditionals: Theory and applications - Miroslav Ristic, Bozidar V. Popovic, Indranil Ghosh -- 8. Assessment of distributional goodness-of-fit for modeling the superposition of renewal process data - Wei Zhang, William Q. Meeker -- 9. Skew-Elliptical Thomas point processes - Ngoc Anh Dao, Marc G. Genton -- 10. Bayesian model assessment and selection using Bregman divergence - Gyuhyeong Goh, Dipak K. Dey -- 11. On hidden truncation in non-normal models - Indranil Ghosh, Hon Keung Tony Ng.
Record Nr. UNISA-996466395303316
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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
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. 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