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Biblioteca

MARC Lista (tabellare)
An Introduction to Statistical Learning : with Applications in R / / by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
An Introduction to Statistical Learning : with Applications in R / / by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Autore James Gareth (Gareth Michael)
Edizione [Second Edition.]
Pubbl/distr/stampa New York, NY : , : Springer US : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (xv, 607 p : il. col.)
Collana Springer Texts in Statistics
Soggetto topico Statistics
Mathematical statistics - Data processing
Artificial intelligence
Statistical Theory and Methods
Statistics and Computing
Artificial Intelligence
Estadística matemàtica
Models matemàtics
R (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 1-0716-1418-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface -- 1 Introduction -- 2 Statistical Learning -- 3 Linear Regression -- 4 Classification -- 5 Resampling Methods -- 6 Linear Model Selection and Regularization -- 7 Moving Beyond Linearity -- 8 Tree-Based Methods -- 9 Support Vector Machines -- 10 Deep Learning -- 11 Survival Analysis and Censored Data -- 12 Unsupervised Learning -- 13 Multiple Testing -- Index.
Record Nr. UNISA-996466401603316
James Gareth (Gareth Michael)  
New York, NY : , : Springer US : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
An Introduction to Statistical Learning : with Applications in R / / by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
An Introduction to Statistical Learning : with Applications in R / / by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Autore James Gareth (Gareth Michael)
Edizione [Second Edition.]
Pubbl/distr/stampa New York, NY : , : Springer US : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (xv, 607 p : il. col.)
Collana Springer Texts in Statistics
Soggetto topico Statistics
Mathematical statistics - Data processing
Artificial intelligence
Statistical Theory and Methods
Statistics and Computing
Artificial Intelligence
Estadística matemàtica
Models matemàtics
R (Llenguatge de programació)
Soggetto genere / forma Llibres electrònics
ISBN 1-0716-1418-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface -- 1 Introduction -- 2 Statistical Learning -- 3 Linear Regression -- 4 Classification -- 5 Resampling Methods -- 6 Linear Model Selection and Regularization -- 7 Moving Beyond Linearity -- 8 Tree-Based Methods -- 9 Support Vector Machines -- 10 Deep Learning -- 11 Survival Analysis and Censored Data -- 12 Unsupervised Learning -- 13 Multiple Testing -- Index.
Record Nr. UNINA-9910495188803321
James Gareth (Gareth Michael)  
New York, NY : , : Springer US : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to statistics with Python : with applications in the life sciences / / Thomas Haslwanter
An introduction to statistics with Python : with applications in the life sciences / / Thomas Haslwanter
Autore Haslwanter Thomas <1964->
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (341 pages)
Disciplina 610.727
Collana Statistics and Computing
Soggetto topico Biometry
Computer science - Mathematics
Programming languages (Electronic computers)
Python (Llenguatge de programació)
Estadística matemàtica
Processament de dades
Biometria
Soggetto genere / forma Llibres electrònics
ISBN 9783030973711
9783030973704
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910631094303321
Haslwanter Thomas <1964->  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
An introduction to statistics with Python : with applications in the life sciences / / Thomas Haslwanter
An introduction to statistics with Python : with applications in the life sciences / / Thomas Haslwanter
Autore Haslwanter Thomas <1964->
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (341 pages)
Disciplina 610.727
Collana Statistics and Computing
Soggetto topico Biometry
Computer science - Mathematics
Programming languages (Electronic computers)
Python (Llenguatge de programació)
Estadística matemàtica
Processament de dades
Biometria
Soggetto genere / forma Llibres electrònics
ISBN 9783030973711
9783030973704
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996499869703316
Haslwanter Thomas <1964->  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Introductory Statistics for Data Analysis [[electronic resource] /] / by Warren J. Ewens, Katherine Brumberg
Introductory Statistics for Data Analysis [[electronic resource] /] / by Warren J. Ewens, Katherine Brumberg
Autore Ewens Warren J.
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (272 pages)
Disciplina 780
Soggetto topico Statistics
Nonparametric statistics
Biometry
Statistical Theory and Methods
Non-parametric Inference
Biostatistics
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783031281891
9783031281884
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto I INTRODUCTION -- 1 Statistics and probability theory -- II PROBABILITY THEORY -- 2 Events -- 3 Probabilities of events -- 4 Probability: One Discrete Random Variable -- 5 Many Random Variables -- 6 Continuous Random Variables -- III STATISTICS -- 7 Introduction -- 8 Estimation of a parameter -- 9 Testing hypotheses about the value of a parameter -- 10 Testing for the equality of two binomial parameters -- 10 Testing for the equality of two binomial parameters -- 11 Chi-square tests (i): tables bigger than two-by-two -- 13 Tests on means -- 14 Non-parametric tests -- Useful charts -- Solutions to problems.
Record Nr. UNINA-9910725089403321
Ewens Warren J.  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Linear and generalized linear mixed models and their applications / / Jiming Jiang and Thuan Nguyen
Linear and generalized linear mixed models and their applications / / Jiming Jiang and Thuan Nguyen
Autore Jiang Jiming
Edizione [Second edition.]
Pubbl/distr/stampa New York, New York ; ; London, England : , : Springer, , [2021]
Descrizione fisica 1 online resource (352 pages) : illustrations
Disciplina 519.5
Collana Springer Series in Statistics
Soggetto topico Mathematical statistics
Linear models (Statistics)
Estadística matemàtica
Models lineals (Estadística)
Soggetto genere / forma Llibres electrònics
ISBN 1-0716-1282-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- List of Notations -- 1 Linear Mixed Models: Part I -- 1.1 Introduction -- 1.1.1 Effect of Air Pollution Episodes on Children -- 1.1.2 Genome-Wide Association Study -- 1.1.3 Small Area Estimation of Income -- 1.2 Types of Linear Mixed Models -- 1.2.1 Gaussian Mixed Models -- 1.2.1.1 Mixed ANOVA Model -- 1.2.1.2 Longitudinal Model -- 1.2.1.3 Marginal Model -- 1.2.1.4 Hierarchical Models -- 1.2.2 Non-Gaussian Linear Mixed Models -- 1.2.2.1 Mixed ANOVA Model -- 1.2.2.2 Longitudinal Model -- 1.2.2.3 Marginal Model -- 1.3 Estimation in Gaussian Mixed Models -- 1.3.1 Maximum Likelihood -- 1.3.1.1 Point Estimation -- 1.3.1.2 Asymptotic Covariance Matrix -- 1.3.2 Restricted Maximum Likelihood (REML) -- 1.3.2.1 Point Estimation -- 1.3.2.2 Historical Note -- 1.3.2.3 Asymptotic Covariance Matrix -- 1.4 Estimation in Non-Gaussian Linear Mixed Models -- 1.4.1 Quasi-Likelihood Method -- 1.4.2 Partially Observed Information -- 1.4.3 Iterative Weighted Least Squares -- 1.4.3.1 Balanced Case -- 1.4.3.2 Unbalanced Case -- 1.4.4 Jackknife Method -- 1.4.5 High-Dimensional Misspecified Mixed Model Analysis -- 1.5 Other Methods of Estimation -- 1.5.1 Analysis of Variance Estimation -- 1.5.1.1 Balanced Data -- 1.5.1.2 Unbalanced Data -- 1.5.2 Minimum Norm Quadratic Unbiased Estimation -- 1.6 Notes on Computation and Software -- 1.6.1 Notes on Computation -- 1.6.1.1 Computation of the ML and REML Estimators -- 1.6.1.2 The EM Algorithm -- 1.6.2 Notes on Software -- 1.7 Real-Life Data Examples -- 1.7.1 Analysis of Birth Weights of Lambs -- 1.7.2 Analysis of Hip Replacements Data -- 1.7.3 Analyses of High-Dimensional GWAS Data -- 1.8 Further Results and Technical Notes -- 1.8.1 A Note on Finding the MLE -- 1.8.2 Note on Matrix X Not Being Full Rank -- 1.8.3 Asymptotic Behavior of ML and REML Estimators in Non-Gaussian Mixed ANOVA Models.
1.8.4 Truncated Estimator -- 1.8.5 POQUIM in General -- 1.9 Exercises -- 2 Linear Mixed Models: Part II -- 2.1 Tests in Linear Mixed Models -- 2.1.1 Tests in Gaussian Mixed Models -- 2.1.1.1 Exact Tests -- 2.1.1.2 Optimal Tests -- 2.1.1.3 Likelihood-Ratio Tests -- 2.1.2 Tests in Non-Gaussian Linear Mixed Models -- 2.1.2.1 Empirical Method of Moments -- 2.1.2.2 Partially Observed Information -- 2.1.2.3 Jackknife Method -- 2.1.2.4 Robust Versions of Classical Tests -- 2.2 Confidence Intervals in Linear Mixed Models -- 2.2.1 Confidence Intervals in Gaussian Mixed Models -- 2.2.1.1 Exact Confidence Intervals for Variance Components -- 2.2.1.2 Approximate Confidence Intervals for Variance Components -- 2.2.1.3 Simultaneous Confidence Intervals -- 2.2.1.4 Confidence Intervals for Fixed Effects -- 2.2.2 Confidence Intervals in Non-Gaussian Linear MixedModels -- 2.2.2.1 ANOVA Models -- 2.2.2.2 Longitudinal Models -- 2.3 Prediction -- 2.3.1 Best Prediction -- 2.3.2 Best Linear Unbiased Prediction -- 2.3.2.1 Empirical BLUP -- 2.3.3 Observed Best Prediction -- 2.3.4 Prediction of Future Observation -- 2.3.4.1 Distribution-Free Prediction Intervals -- 2.3.4.2 Standard Linear Mixed Models -- 2.3.4.3 Nonstandard Linear Mixed Models -- 2.3.4.4 A Simulated Example -- 2.3.5 Classified Mixed Model Prediction -- 2.3.5.1 CMMP of Mixed Effects -- 2.3.5.2 CMMP of Future Observation -- 2.3.5.3 CMMP When the Actual Match Does Not Exist -- 2.3.5.4 Empirical Demonstration -- 2.3.5.5 Incorporating Covariate Information in Matching -- 2.3.5.6 More Empirical Demonstration -- 2.3.5.7 Prediction Interval -- 2.4 Model Checking and Selection -- 2.4.1 Model Diagnostics -- 2.4.1.1 Diagnostic Plots -- 2.4.1.2 Goodness-of-Fit Tests -- 2.4.2 Information Criteria -- 2.4.2.1 Selection with Fixed Random Factors -- 2.4.2.2 Selection with Random Factors -- 2.4.3 The Fence Methods.
2.4.3.1 The Effective Sample Size -- 2.4.3.2 The Dimension of a Model -- 2.4.3.3 Unknown Distribution -- 2.4.3.4 Finite-Sample Performance and the Effect of a Constant -- 2.4.3.5 Criterion of Optimality -- 2.4.4 Shrinkage Mixed Model Selection -- 2.5 Bayesian Inference -- 2.5.1 Inference About Variance Components -- 2.5.2 Inference About Fixed and Random Effects -- 2.6 Real-Life Data Examples -- 2.6.1 Reliability of Environmental Sampling -- 2.6.2 Hospital Data -- 2.6.3 Baseball Example -- 2.6.4 Iowa Crops Data -- 2.6.5 Analysis of High-Speed Network Data -- 2.7 Further Results and Technical Notes -- 2.7.1 Robust Versions of Classical Tests -- 2.7.2 Existence of Moments of ML/REML Estimators -- 2.7.3 Existence of Moments of EBLUE and EBLUP -- 2.7.4 The Definition of Σn(θ) in Sect.2.4.1.2 -- 2.8 Exercises -- 3 Generalized Linear Mixed Models: Part I -- 3.1 Introduction -- 3.2 Generalized Linear Mixed Models -- 3.3 Real-Life Data Examples -- 3.3.1 Salamander Mating Experiments -- 3.3.2 A Log-Linear Mixed Model for Seizure Counts -- 3.3.3 Small Area Estimation of Mammography Rates -- 3.4 Likelihood Function Under GLMM -- 3.5 Approximate Inference -- 3.5.1 Laplace Approximation -- 3.5.2 Penalized Quasi-likelihood Estimation -- 3.5.2.1 Derivation of PQL -- 3.5.2.2 Computational Procedures -- 3.5.2.3 Variance Components -- 3.5.2.4 Inconsistency of PQL Estimators -- 3.5.3 Tests of Zero Variance Components -- 3.5.4 Maximum Hierarchical Likelihood -- 3.5.5 Note on Existing Software -- 3.6 GLMM Prediction -- 3.6.1 Joint Estimation of Fixed and Random Effects -- 3.6.1.1 Maximum a Posterior -- 3.6.1.2 Computation of MPE -- 3.6.1.3 Penalized Generalized WLS -- 3.6.1.4 Maximum Conditional Likelihood -- 3.6.1.5 Quadratic Inference Function -- 3.6.2 Empirical Best Prediction -- 3.6.2.1 Empirical Best Prediction Under GLMM -- 3.6.2.2 Model-Assisted EBP.
3.6.3 A Simulated Example -- 3.6.4 Classified Mixed Logistic Model Prediction -- 3.6.5 Best Look-Alike Prediction -- 3.6.5.1 BLAP of a Discrete/Categorical Random Variable -- 3.6.5.2 BLAP of a Zero-Inflated Random Variable -- 3.7 Real-Life Data Example Follow-Ups and More -- 3.7.1 Salamander Mating Data -- 3.7.2 Seizure Count Data -- 3.7.3 Mammography Rates -- 3.7.4 Analysis of ECMO Data -- 3.7.4.1 Prediction of Mixed Effects of Interest -- 3.8 Further Results and Technical Notes -- 3.8.1 More on NLGSA -- 3.8.2 Asymptotic Properties of PQWLS Estimators -- 3.8.3 MSPE of EBP -- 3.8.4 MSPE of the Model-Assisted EBP -- 3.9 Exercises -- 4 Generalized Linear Mixed Models: Part II -- 4.1 Likelihood-Based Inference -- 4.1.1 A Monte Carlo EM Algorithm for Binary Data -- 4.1.1.1 The EM Algorithm -- 4.1.1.2 Monte Carlo EM via Gibbs Sampler -- 4.1.2 Extensions -- 4.1.2.1 MCEM with Metropolis-Hastings Algorithm -- 4.1.2.2 Monte Carlo Newton-Raphson Procedure -- 4.1.2.3 Simulated ML -- 4.1.3 MCEM with i.i.d. Sampling -- 4.1.3.1 Importance Sampling -- 4.1.3.2 Rejection Sampling -- 4.1.4 Automation -- 4.1.5 Data Cloning -- 4.1.6 Maximization by Parts -- 4.1.7 Bayesian Inference -- 4.2 Estimating Equations -- 4.2.1 Generalized Estimating Equations (GEE) -- 4.2.2 Iterative Estimating Equations -- 4.2.3 Method of Simulated Moments -- 4.2.4 Robust Estimation in GLMM -- 4.3 GLMM Diagnostics and Selection -- 4.3.1 A Goodness-of-Fit Test for GLMM Diagnostics -- 4.3.1.1 Tailoring -- 4.3.1.2 χ2-Test -- 4.3.1.3 Application to GLMM -- 4.3.2 Fence Methods for GLMM Selection -- 4.3.2.1 Maximum Likelihood (ML) Model Selection -- 4.3.2.2 Mean and Variance/Covariance (MVC) Model Selection -- 4.3.2.3 Extended GLMM Selection -- 4.3.3 Two Examples with Simulation -- 4.3.3.1 A Simulated Example of GLMM Diagnostics -- 4.3.3.2 A Simulated Example of GLMM Selection.
4.4 Real-Life Data Examples -- 4.4.1 Fetal Mortality in Mouse Litters -- 4.4.2 Analysis of Gc Genotype Data -- 4.4.3 Salamander Mating Experiments Revisited -- 4.4.4 The National Health Interview Survey -- 4.5 Further Results and Technical Notes -- 4.5.1 Proof of Theorem 4.3 -- 4.5.2 Linear Convergence and Asymptotic Properties of IEE -- 4.5.2.1 Linear Convergence -- 4.5.2.2 Asymptotic Behavior of IEEE -- 4.5.3 Incorporating Informative Missing Data in IEE -- 4.5.4 Consistency of MSM Estimator -- 4.5.5 Asymptotic Properties of First- and Second-StepEstimators -- 4.5.6 Further Details Regarding the Fence Methods -- 4.5.6.1 Estimation of σM,M* in Case of Clustered Observations -- 4.5.6.2 Consistency of the Fence -- 4.5.7 Consistency of MLE in GLMM with Crossed Random Effects -- 4.6 Exercises -- A Matrix Algebra -- A.1 Kronecker Products -- A.2 Matrix Differentiation -- A.3 Projection and Related Results -- A.4 Inverse and Generalized Inverse -- A.5 Decompositions of Matrices -- A.6 The Eigenvalue Perturbation Theory -- B Some Results in Statistics -- B.1 Multivariate Normal Distribution -- B.2 Quadratic Forms -- B.3 OP and oP -- B.4 Convolution -- B.5 Exponential Family and Generalized Linear Models -- References -- Index.
Record Nr. UNISA-996466561103316
Jiang Jiming  
New York, New York ; ; London, England : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Linear and generalized linear mixed models and their applications / / Jiming Jiang and Thuan Nguyen
Linear and generalized linear mixed models and their applications / / Jiming Jiang and Thuan Nguyen
Autore Jiang Jiming
Edizione [Second edition.]
Pubbl/distr/stampa New York, New York ; ; London, England : , : Springer, , [2021]
Descrizione fisica 1 online resource (352 pages) : illustrations
Disciplina 519.5
Collana Springer Series in Statistics
Soggetto topico Mathematical statistics
Linear models (Statistics)
Estadística matemàtica
Models lineals (Estadística)
Soggetto genere / forma Llibres electrònics
ISBN 1-0716-1282-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- List of Notations -- 1 Linear Mixed Models: Part I -- 1.1 Introduction -- 1.1.1 Effect of Air Pollution Episodes on Children -- 1.1.2 Genome-Wide Association Study -- 1.1.3 Small Area Estimation of Income -- 1.2 Types of Linear Mixed Models -- 1.2.1 Gaussian Mixed Models -- 1.2.1.1 Mixed ANOVA Model -- 1.2.1.2 Longitudinal Model -- 1.2.1.3 Marginal Model -- 1.2.1.4 Hierarchical Models -- 1.2.2 Non-Gaussian Linear Mixed Models -- 1.2.2.1 Mixed ANOVA Model -- 1.2.2.2 Longitudinal Model -- 1.2.2.3 Marginal Model -- 1.3 Estimation in Gaussian Mixed Models -- 1.3.1 Maximum Likelihood -- 1.3.1.1 Point Estimation -- 1.3.1.2 Asymptotic Covariance Matrix -- 1.3.2 Restricted Maximum Likelihood (REML) -- 1.3.2.1 Point Estimation -- 1.3.2.2 Historical Note -- 1.3.2.3 Asymptotic Covariance Matrix -- 1.4 Estimation in Non-Gaussian Linear Mixed Models -- 1.4.1 Quasi-Likelihood Method -- 1.4.2 Partially Observed Information -- 1.4.3 Iterative Weighted Least Squares -- 1.4.3.1 Balanced Case -- 1.4.3.2 Unbalanced Case -- 1.4.4 Jackknife Method -- 1.4.5 High-Dimensional Misspecified Mixed Model Analysis -- 1.5 Other Methods of Estimation -- 1.5.1 Analysis of Variance Estimation -- 1.5.1.1 Balanced Data -- 1.5.1.2 Unbalanced Data -- 1.5.2 Minimum Norm Quadratic Unbiased Estimation -- 1.6 Notes on Computation and Software -- 1.6.1 Notes on Computation -- 1.6.1.1 Computation of the ML and REML Estimators -- 1.6.1.2 The EM Algorithm -- 1.6.2 Notes on Software -- 1.7 Real-Life Data Examples -- 1.7.1 Analysis of Birth Weights of Lambs -- 1.7.2 Analysis of Hip Replacements Data -- 1.7.3 Analyses of High-Dimensional GWAS Data -- 1.8 Further Results and Technical Notes -- 1.8.1 A Note on Finding the MLE -- 1.8.2 Note on Matrix X Not Being Full Rank -- 1.8.3 Asymptotic Behavior of ML and REML Estimators in Non-Gaussian Mixed ANOVA Models.
1.8.4 Truncated Estimator -- 1.8.5 POQUIM in General -- 1.9 Exercises -- 2 Linear Mixed Models: Part II -- 2.1 Tests in Linear Mixed Models -- 2.1.1 Tests in Gaussian Mixed Models -- 2.1.1.1 Exact Tests -- 2.1.1.2 Optimal Tests -- 2.1.1.3 Likelihood-Ratio Tests -- 2.1.2 Tests in Non-Gaussian Linear Mixed Models -- 2.1.2.1 Empirical Method of Moments -- 2.1.2.2 Partially Observed Information -- 2.1.2.3 Jackknife Method -- 2.1.2.4 Robust Versions of Classical Tests -- 2.2 Confidence Intervals in Linear Mixed Models -- 2.2.1 Confidence Intervals in Gaussian Mixed Models -- 2.2.1.1 Exact Confidence Intervals for Variance Components -- 2.2.1.2 Approximate Confidence Intervals for Variance Components -- 2.2.1.3 Simultaneous Confidence Intervals -- 2.2.1.4 Confidence Intervals for Fixed Effects -- 2.2.2 Confidence Intervals in Non-Gaussian Linear MixedModels -- 2.2.2.1 ANOVA Models -- 2.2.2.2 Longitudinal Models -- 2.3 Prediction -- 2.3.1 Best Prediction -- 2.3.2 Best Linear Unbiased Prediction -- 2.3.2.1 Empirical BLUP -- 2.3.3 Observed Best Prediction -- 2.3.4 Prediction of Future Observation -- 2.3.4.1 Distribution-Free Prediction Intervals -- 2.3.4.2 Standard Linear Mixed Models -- 2.3.4.3 Nonstandard Linear Mixed Models -- 2.3.4.4 A Simulated Example -- 2.3.5 Classified Mixed Model Prediction -- 2.3.5.1 CMMP of Mixed Effects -- 2.3.5.2 CMMP of Future Observation -- 2.3.5.3 CMMP When the Actual Match Does Not Exist -- 2.3.5.4 Empirical Demonstration -- 2.3.5.5 Incorporating Covariate Information in Matching -- 2.3.5.6 More Empirical Demonstration -- 2.3.5.7 Prediction Interval -- 2.4 Model Checking and Selection -- 2.4.1 Model Diagnostics -- 2.4.1.1 Diagnostic Plots -- 2.4.1.2 Goodness-of-Fit Tests -- 2.4.2 Information Criteria -- 2.4.2.1 Selection with Fixed Random Factors -- 2.4.2.2 Selection with Random Factors -- 2.4.3 The Fence Methods.
2.4.3.1 The Effective Sample Size -- 2.4.3.2 The Dimension of a Model -- 2.4.3.3 Unknown Distribution -- 2.4.3.4 Finite-Sample Performance and the Effect of a Constant -- 2.4.3.5 Criterion of Optimality -- 2.4.4 Shrinkage Mixed Model Selection -- 2.5 Bayesian Inference -- 2.5.1 Inference About Variance Components -- 2.5.2 Inference About Fixed and Random Effects -- 2.6 Real-Life Data Examples -- 2.6.1 Reliability of Environmental Sampling -- 2.6.2 Hospital Data -- 2.6.3 Baseball Example -- 2.6.4 Iowa Crops Data -- 2.6.5 Analysis of High-Speed Network Data -- 2.7 Further Results and Technical Notes -- 2.7.1 Robust Versions of Classical Tests -- 2.7.2 Existence of Moments of ML/REML Estimators -- 2.7.3 Existence of Moments of EBLUE and EBLUP -- 2.7.4 The Definition of Σn(θ) in Sect.2.4.1.2 -- 2.8 Exercises -- 3 Generalized Linear Mixed Models: Part I -- 3.1 Introduction -- 3.2 Generalized Linear Mixed Models -- 3.3 Real-Life Data Examples -- 3.3.1 Salamander Mating Experiments -- 3.3.2 A Log-Linear Mixed Model for Seizure Counts -- 3.3.3 Small Area Estimation of Mammography Rates -- 3.4 Likelihood Function Under GLMM -- 3.5 Approximate Inference -- 3.5.1 Laplace Approximation -- 3.5.2 Penalized Quasi-likelihood Estimation -- 3.5.2.1 Derivation of PQL -- 3.5.2.2 Computational Procedures -- 3.5.2.3 Variance Components -- 3.5.2.4 Inconsistency of PQL Estimators -- 3.5.3 Tests of Zero Variance Components -- 3.5.4 Maximum Hierarchical Likelihood -- 3.5.5 Note on Existing Software -- 3.6 GLMM Prediction -- 3.6.1 Joint Estimation of Fixed and Random Effects -- 3.6.1.1 Maximum a Posterior -- 3.6.1.2 Computation of MPE -- 3.6.1.3 Penalized Generalized WLS -- 3.6.1.4 Maximum Conditional Likelihood -- 3.6.1.5 Quadratic Inference Function -- 3.6.2 Empirical Best Prediction -- 3.6.2.1 Empirical Best Prediction Under GLMM -- 3.6.2.2 Model-Assisted EBP.
3.6.3 A Simulated Example -- 3.6.4 Classified Mixed Logistic Model Prediction -- 3.6.5 Best Look-Alike Prediction -- 3.6.5.1 BLAP of a Discrete/Categorical Random Variable -- 3.6.5.2 BLAP of a Zero-Inflated Random Variable -- 3.7 Real-Life Data Example Follow-Ups and More -- 3.7.1 Salamander Mating Data -- 3.7.2 Seizure Count Data -- 3.7.3 Mammography Rates -- 3.7.4 Analysis of ECMO Data -- 3.7.4.1 Prediction of Mixed Effects of Interest -- 3.8 Further Results and Technical Notes -- 3.8.1 More on NLGSA -- 3.8.2 Asymptotic Properties of PQWLS Estimators -- 3.8.3 MSPE of EBP -- 3.8.4 MSPE of the Model-Assisted EBP -- 3.9 Exercises -- 4 Generalized Linear Mixed Models: Part II -- 4.1 Likelihood-Based Inference -- 4.1.1 A Monte Carlo EM Algorithm for Binary Data -- 4.1.1.1 The EM Algorithm -- 4.1.1.2 Monte Carlo EM via Gibbs Sampler -- 4.1.2 Extensions -- 4.1.2.1 MCEM with Metropolis-Hastings Algorithm -- 4.1.2.2 Monte Carlo Newton-Raphson Procedure -- 4.1.2.3 Simulated ML -- 4.1.3 MCEM with i.i.d. Sampling -- 4.1.3.1 Importance Sampling -- 4.1.3.2 Rejection Sampling -- 4.1.4 Automation -- 4.1.5 Data Cloning -- 4.1.6 Maximization by Parts -- 4.1.7 Bayesian Inference -- 4.2 Estimating Equations -- 4.2.1 Generalized Estimating Equations (GEE) -- 4.2.2 Iterative Estimating Equations -- 4.2.3 Method of Simulated Moments -- 4.2.4 Robust Estimation in GLMM -- 4.3 GLMM Diagnostics and Selection -- 4.3.1 A Goodness-of-Fit Test for GLMM Diagnostics -- 4.3.1.1 Tailoring -- 4.3.1.2 χ2-Test -- 4.3.1.3 Application to GLMM -- 4.3.2 Fence Methods for GLMM Selection -- 4.3.2.1 Maximum Likelihood (ML) Model Selection -- 4.3.2.2 Mean and Variance/Covariance (MVC) Model Selection -- 4.3.2.3 Extended GLMM Selection -- 4.3.3 Two Examples with Simulation -- 4.3.3.1 A Simulated Example of GLMM Diagnostics -- 4.3.3.2 A Simulated Example of GLMM Selection.
4.4 Real-Life Data Examples -- 4.4.1 Fetal Mortality in Mouse Litters -- 4.4.2 Analysis of Gc Genotype Data -- 4.4.3 Salamander Mating Experiments Revisited -- 4.4.4 The National Health Interview Survey -- 4.5 Further Results and Technical Notes -- 4.5.1 Proof of Theorem 4.3 -- 4.5.2 Linear Convergence and Asymptotic Properties of IEE -- 4.5.2.1 Linear Convergence -- 4.5.2.2 Asymptotic Behavior of IEEE -- 4.5.3 Incorporating Informative Missing Data in IEE -- 4.5.4 Consistency of MSM Estimator -- 4.5.5 Asymptotic Properties of First- and Second-StepEstimators -- 4.5.6 Further Details Regarding the Fence Methods -- 4.5.6.1 Estimation of σM,M* in Case of Clustered Observations -- 4.5.6.2 Consistency of the Fence -- 4.5.7 Consistency of MLE in GLMM with Crossed Random Effects -- 4.6 Exercises -- A Matrix Algebra -- A.1 Kronecker Products -- A.2 Matrix Differentiation -- A.3 Projection and Related Results -- A.4 Inverse and Generalized Inverse -- A.5 Decompositions of Matrices -- A.6 The Eigenvalue Perturbation Theory -- B Some Results in Statistics -- B.1 Multivariate Normal Distribution -- B.2 Quadratic Forms -- B.3 OP and oP -- B.4 Convolution -- B.5 Exponential Family and Generalized Linear Models -- References -- Index.
Record Nr. UNINA-9910484963903321
Jiang Jiming  
New York, New York ; ; London, England : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Markov Renewal and Piecewise Deterministic Processes [[electronic resource] /] / by Christiane Cocozza-Thivent
Markov Renewal and Piecewise Deterministic Processes [[electronic resource] /] / by Christiane Cocozza-Thivent
Autore Cocozza-Thivent Christiane
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XIV, 252 p. 16 illus., 4 illus. in color.)
Disciplina 519.233
Collana Probability Theory and Stochastic Modelling
Soggetto topico Markov processes
Computer science - Mathematics
Mathematical statistics
Markov Process
Probability and Statistics in Computer Science
Processos de Markov
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-70447-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Tools -- Markov renewal processes and related processes -- First steps with PDMP -- Hitting time distribution -- Intensity of some marked point pocesses -- Generalized Kolmogorov equations -- A martingale approach -- Stability -- Numerical methods -- Switching Processes -- Tools -- Interarrival distribution with several Dirac measures -- Algorithm convergence's proof.
Record Nr. UNINA-9910484004403321
Cocozza-Thivent Christiane  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Markov Renewal and Piecewise Deterministic Processes [[electronic resource] /] / by Christiane Cocozza-Thivent
Markov Renewal and Piecewise Deterministic Processes [[electronic resource] /] / by Christiane Cocozza-Thivent
Autore Cocozza-Thivent Christiane
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XIV, 252 p. 16 illus., 4 illus. in color.)
Disciplina 519.233
Collana Probability Theory and Stochastic Modelling
Soggetto topico Markov processes
Computer science - Mathematics
Mathematical statistics
Markov Process
Probability and Statistics in Computer Science
Processos de Markov
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-70447-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Tools -- Markov renewal processes and related processes -- First steps with PDMP -- Hitting time distribution -- Intensity of some marked point pocesses -- Generalized Kolmogorov equations -- A martingale approach -- Stability -- Numerical methods -- Switching Processes -- Tools -- Interarrival distribution with several Dirac measures -- Algorithm convergence's proof.
Record Nr. UNISA-996466393303316
Cocozza-Thivent Christiane  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Mathematical and statistical methods for actuarial sciences and finance / / edited by Marco Corazza [and three others]
Mathematical and statistical methods for actuarial sciences and finance / / edited by Marco Corazza [and three others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (456 pages)
Disciplina 368.01
Soggetto topico Finance - Statistical methods
Finance - Mathematical models
Insurance - Mathematical models
Matemàtica actuarial
Finances
Models matemàtics
Estadística matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-030-99638-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- Absolute and Relative Gender Gap in Pensions: The Impact of the Transition from DB to NDC in Italy -- 1 Introduction -- 2 Data and Methodology -- 2.1 Data -- 2.2 Methodology -- 3 Preliminary Results -- 4 Remarks -- References -- TPPI: Textual Political Polarity Indices. The Case of Italian GDP -- 1 Introduction -- 2 Data -- 2.1 The Italian Senate Verbatim Reports -- 2.2 The Italian Yearly GDP Time Series -- 3 Determining Words Sentiment Polarities -- 4 Polarity Indices Time Series -- 4.1 Total Textual Political Polarity Index (TPPI-T) -- 4.2 Group Specific Textual Political Polarity Indices (TPPI-GS) -- 4.3 Polarity Divergence Indices (TPPI-D) -- 5 Evaluating Indices Configurations -- 6 Conclusion -- References -- Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data -- 1 Introduction -- 2 Methodology -- 3 Empirical Application -- 4 Conclusions -- References -- Gender Attitudes Toward Longevity and Retirement Planning: Theory and Evidence -- 1 Introduction -- 2 Drivers of Retirement Behaviour: the State-of-the-Art -- 3 Subjective Longevity, Gender and Economic Choices -- 4 Our Research Framework and Directions -- References -- Semiclassical Pricing of Variance Swaps in the CEV Model -- 1 Introduction -- 2 The Model -- 2.1 Variance Swap Pricing -- 3 Realized Variance Replication -- 3.1 The Semiclassical Approximation for the Log Contract -- 4 Numerical Results -- References -- Indexing Pensions to Life Expectancy: Keeping the System Fair Across Generations -- 1 Introduction -- 2 Intergenerational Fairness and Neutrality Condition -- 3 Policy Options -- 3.1 Adjusting the Contribution Rate -- 3.2 Adjusting the Retirement Age While Keeping the Replacement Rate Constant -- 3.3 Adjusting the Retirement Age While Improving Pension Adequacy.
3.4 Amending Entry Pensions Through a Sustainability Factor -- 4 Conclusion -- References -- Dynamic Withdrawals and Stochastic Mortality in GLWB Variable Annuities -- 1 Introduction -- 2 The Contract Structure -- 3 The Valuation Framework -- 4 Dynamic Programming -- 4.1 Bang-Bang Analysis -- 4.2 Contract Decomposition -- 5 Conclusion -- References -- A Regression Based Approach for Valuing Longevity Measures -- 1 Introduction -- 2 Life Expectancy and Computational Framework -- 2.1 Valuation Procedure -- 3 Numerical Results -- 4 Conclusion -- References -- On the Assessment of the Payment Limitation for an Health Plan -- 1 Introduction -- 2 Actuarial Framework -- 3 The Optimal Reimbursement Problem -- 4 Numerical Investigation -- 5 Conclusions -- References -- Reference Dependence in Behavioral Portfolio Selection -- 1 Introduction -- 2 Behavioral Portfolio Selection -- 3 The Reference Point -- 4 An Application -- References -- Pricing Rainfall Derivatives by Genetic Programming: A Case Study -- 1 Introduction -- 2 Genetic Programming -- 3 Rainfall Derivatives Pricing -- 4 Data and Application -- 5 Conclusion -- References -- Estimation of the Gift Probability in Fund Raising Management -- 1 Introduction -- 2 The Donor -- 3 Modeling the Gift as an Individual Risk -- 4 Poisson Regression in FR -- References -- The Estimation Risk in Credit Regulatory Capital -- 1 Introduction -- 2 The Capital Requirement in the IRB Approach -- 3 The Dataset and Parameters' Gaussian Copula -- 4 Estimation Risk in RC and Policy Implication -- References -- Actuarial Fairness in Pension Systems: An Empirical Evaluation for Italy Using an OLG Model -- 1 Introduction -- 2 Methods -- 3 Main Results -- 4 Discussion and Conclusions -- References -- Forecasting VIX with Hurst Exponent -- 1 Introduction -- 2 Model and Estimator -- 3 Empirical Analysis and Results.
4 Conclusions and Further Directions -- References -- Modelling H-Volatility with Fractional Brownian Bridge -- 1 Introduction -- 2 Fractional Brownian Bridge -- 3 Methodology and Application -- 4 Conclusion -- References -- Shapley Value in Partition Function Form Games: New Research Perspectives for Features Selection -- 1 Introduction -- 2 Games in Partition Function Form -- 2.1 The Shapley Value -- 3 Shapley Values for Features Contributions -- 4 Conclusions and Further Research -- References -- Nonparametric Estimation of Range Value at Risk -- 1 Introduction -- 1.1 Definitions -- 2 Nonparametric Methods for Estimating RVaR -- 2.1 Empirical Estimator -- 2.2 Brazauskas et al.'s Estimator -- 2.3 Kernel Estimator -- 2.4 Yamai and Yoshiba's Estimator -- 2.5 Filtered Historical Method -- 3 Simulation -- 4 Findings -- References -- A Fixed Career Length Versus a Fixed Retirement Age: An Analysis per Socio-Economic Groups -- 1 Introduction -- 2 Objective -- 3 Actuarial Fairness -- 4 Data -- 5 Policy Implications -- References -- Nonparametric Test for Financial Time Series Comparisons -- 1 Introduction -- 2 Statistical Problem -- 3 Methodological Solution -- 4 Case Study -- 5 Concluding Remarks -- References -- Innovative Parametric Weather Insurance on Satellite Data in Agribusiness -- 1 Introduction -- 2 Methodology and Satellite Data -- 3 Personalised Parametric Weather Insurance -- 4 Numerical Application -- 5 Concluding Remarks -- References -- An Application of the Tensor-Based Approach to Mortality Modeling -- 1 Introduction -- 2 Methodology and Application -- 3 Conclusions -- References -- Cyber Risk: Estimates for Malicious and Negligent Breaches Distributions -- 1 Introduction -- 2 Cyber Incidents and Data Breaches -- 3 Case Study -- 4 Concluding Remarks -- References.
Modeling and Forecasting Natural Gas Futures Prices Dynamics: An Integrated Approach -- 1 Introduction -- 2 Data and Methods -- 3 Empirical Results -- 4 Conclusion -- A Appendix: Figures -- References -- Modelling Life Expectancy Gender Gap in a Multi-population Framework -- 1 Introduction -- 2 Materials and Methods -- 3 Results -- 4 Conclusions -- References -- Decision Making in Portfolio Optimization by Using a Tri-Objective Model and Decision Parameters -- 1 Introduction and Motivation of the Study -- 2 Study Framework and Experimental Results -- 3 Conclusions -- References -- Bitcoin Price Prediction: Mixed Integer Quadratic Programming Versus Machine Learning Approaches -- 1 Introduction -- 2 Our Problem -- 2.1 Our MIP Viewpoint vs. SVMs -- References -- Verifying the Rényi Dependence Axioms for a Non-linear Bivariate Comovement Index -- 1 Introduction -- 2 The Comovement Index and the Rényi Dependence Axioms -- 3 Is 1 , 2 a Measure of Dependence à la Rényi? -- References -- Inflation Perceptions and Expectations During the Pandemic: A Model Based Approach -- 1 Introduction -- 2 The Model -- 3 Results -- 4 Conclusions -- References -- A Proposal to Calculate the Regulatory Capital Requirements for Reverse Mortgages -- 1 Introduction -- 2 Modeling House Price Risk, Interest Rate Risk and Mortality Rate Dynamics -- 3 Calculation of Regulatory Capital Requirements -- References -- LTC of a Defined Benefit Employee Pension Scheme -- 1 Introduction -- 2 The Model -- 3 A Sample for Spain -- 3.1 Mortality Tables by State -- 3.2 Results -- 4 Conclusions -- References -- Socio-Economic Challenges at the Time of COVID-19: The Proactive Role of the Insurance Industry -- 1 Introduction -- 2 Sustainability and Impact: A Possible Conjugation -- 2.1 The Guidelines of the Scheme -- 2.2 Which Category Within Socially Responsible Investments? -- References.
Feynman-Kac Formula for BSDEs with Jumps and Time Delayed Generators Associated to Path-Dependent Nonlinear Kolmogorov Equations -- 1 The Non-linear Path Dependent Kolmogorov Equation -- 2 The FBSDE System -- 3 Feynman-Kac Formula -- 4 Financial Applications -- 4.1 The Large Investor Problem -- 4.2 Dynamic Risk Measure for an Insurance Payment Process -- References -- The Role of Stablecoins: Cryptocurrencies Sought Stability and Found Gold and Dollars -- 1 Introduction -- 2 Methodology -- 2.1 The Portfolio Allocation Method -- 2.2 Downside Risk Measures and Backtesting -- 3 Main Results and Findings -- References -- Interbank Networks and Liquidity Risk -- 1 Introduction -- 2 A Model of Liquidity Dynamics on an Interbank Network -- 3 Numerical Simulations with Diagnostic of Network Efficiency -- 4 Conclusions and Research Perspectives -- References -- Kendall Conditional Value-at-Risk -- 1 Introduction -- 2 The Kendall CoVaR -- 3 Illustration: Analysis of the Italian banking systems -- References -- Daily Trading of the FTSE Index Using LSTM with Principal Component Analysis -- 1 Introduction -- 2 Related Work -- 2.1 Ensemble Methods -- 2.2 Hybrid Methods -- 2.3 Deep Learning Paradigms -- 3 Model Architecture -- 3.1 Overview -- 3.2 Sub-Learners -- 3.3 Meta-learners -- 4 Methods -- 4.1 Creating the Dataset -- 5 Experimental Setup and Evaluation -- 6 Results -- 7 Conclusion -- References -- A Hybrid Model Based on Stochastic Volatility and Machine Learning to Forecast Log Returns of a Risky Asset -- 1 Introduction -- 2 The Hybrid Model -- 3 Numerical Experiments -- References -- Financial Time Series Classification by Nonparametric Trend Estimation -- 1 Introduction -- 2 The Proposed Method -- 3 Real Data Application -- 4 Conclusions -- References -- Differential Pursuit-Evasion Games and Space Economy: New Research Perspectives -- 1 Introduction.
2 Space Economy and the Detritus Management: The Role of Differential Games.
Record Nr. UNISA-996472038703316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui