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Biblioteca

MARC Lista (tabellare)
How data quality affects our understanding of the earnings distribution / / Reza C. Daniels
How data quality affects our understanding of the earnings distribution / / Reza C. Daniels
Autore Daniels Reza Che
Pubbl/distr/stampa Singapore, : Springer Nature, 2022
Descrizione fisica 1 online resource (xx, 114 pages) : illustrations (some color)
Soggetto topico Income distribution - Statistical methods
Mathematical statistics
Distribució de la renda
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
Soggetto non controllato Methodology for Collecting
Estimating and Organizing Microeconomic Data
Survey Methods
Total Survey Error
Response Propensity Models
Multiple Imputation
Income Distribution
ISBN 981-19-3639-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys Questionnaire Design and Response Propensities for Labour Income Micro Data Univariate Multiple Imputation for Coarse Employee Income Data Conclusion: How Data Quality Affects our Understanding of the Earnings Distribution
Record Nr. UNINA-9910580174103321
Daniels Reza Che  
Singapore, : Springer Nature, 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Important applications of the Behrens-Fisher statistic and the false discovery rate / / Tejas A. Desai
Important applications of the Behrens-Fisher statistic and the false discovery rate / / Tejas A. Desai
Autore Desai Tejas A.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (88 pages)
Disciplina 519.538
Collana SpringerBriefs in statistics
Soggetto topico Analysis of variance
Mathematical statistics
Anàlisi de variància
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783030998882
9783030998875
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996479367003316
Desai Tejas A.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Important applications of the Behrens-Fisher statistic and the false discovery rate / / Tejas A. Desai
Important applications of the Behrens-Fisher statistic and the false discovery rate / / Tejas A. Desai
Autore Desai Tejas A.
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (88 pages)
Disciplina 519.538
Collana SpringerBriefs in statistics
Soggetto topico Analysis of variance
Mathematical statistics
Anàlisi de variància
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783030998882
9783030998875
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910578693803321
Desai Tejas A.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Interdisciplinary statistics in Mexico : AME virtual meeting, September 10-11, 2020, and 34 FNE, Acatlán, Mexico, September 22-24, 2021 / / Isadora Antoniano-Villalobos [and three others] editors
Interdisciplinary statistics in Mexico : AME virtual meeting, September 10-11, 2020, and 34 FNE, Acatlán, Mexico, September 22-24, 2021 / / Isadora Antoniano-Villalobos [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (234 pages)
Disciplina 519.5
Collana Springer proceedings in mathematics & statistics
Soggetto topico Mathematical statistics
Statistics
Estadística matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-031-12778-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- A Methodological Proposal to Model and Evaluate the Complexity of the Mexican Geo-Electoral System -- 1 Introduction -- 2 Methodological Framework -- 2.1 Conceptualization of Electoral Complexity -- 2.2 Statistical Indicators for Quantifying Electoral Complexity -- 2.3 Data Transformation, Electoral Complexity Indices, and Stratification -- 3 ECI Construction and Stratification -- 3.1 Exploratory Analysis and PCA Implementation -- 3.2 Clustering Analysis and Stratification with K-Means -- 4 Electoral Complexity Ranking and Stratification Results -- 5 Conclusions -- References -- A Spatial Analysis of Drug Dealing in Mexico City -- 1 Introduction -- 2 Background -- 2.1 Drug Dealing as Part of Organized Crime -- 2.2 Research Approaches -- 3 Methodology -- 4 Results -- 4.1 Univariate Global and Local Moran's I -- 4.2 Bivariate Global and Local Moran's I -- 5 Discussion and Conclusions -- References -- Bayesian Hierarchical Multinomial Modeling of the 2021 Mexican Election Outcomes with Censored Samples -- 1 Introduction -- 2 Background -- 2.1 Estimation Methods in the 2021 Quick Count -- 2.2 NBM Model Background -- 2.3 The Bias Problem -- 3 The NBM Model -- 3.1 Specification -- 3.2 Consistency of Our Modeling Assumptions -- 3.3 Fitting Procedure -- 3.4 Model Adequacy -- 4 The 2021 Mexican Elections -- 4.1 Data and Sample Design -- 4.2 Results -- 5 Conclusions and Future Work -- References -- Assessing Hospitalization for SARS-CoV-2 Confirmed Cases by a Cross-Entropy Weighted Ensemble Classifier -- 1 Introduction -- 2 Material -- 2.1 Dataset Description -- 2.2 Data Analysis -- 3 Cross-entropy Weighted Ensemble Classifier -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Emotion Analysis to Identify Risk of Committing Suicide Using Statistical Learning -- 1 Background -- 2 Materials and Methods.
2.1 Emotion Mining -- 2.2 Statistical Learning Methods -- 2.3 Training and Test Datasets -- 3 Results -- 3.1 Supervised Models -- 3.2 Unsupervised Model -- 3.3 Model Comparison -- 3.4 Testing the Models with a New Test Set: COVID-19 -- 4 Conclusions -- References -- Characterizing Groups Using Latent Class Mixed Models: Antiretroviral Treatment Adherence Analysis -- 1 Introduction -- 2 Materials -- 2.1 Population -- 2.2 Data Collection -- 2.3 ART Adherence Definition -- 3 Latent Class Mixture Models -- 4 Results -- 4.1 Latent Classes from ART Adherence -- 4.2 Latent Classes for Bivariate Response of CD4/CD8 Ratio and CD4+T Within Groups of Adherence -- 5 Discussion and Conclusions -- References -- A Dynamic Model for Analyzing the Public Health Policy of the Mexican Government During the COVID-19 Pandemic -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 SIRD Model -- 3.2 Transmission Rate Model -- 3.3 Recovery Rate Estimation -- 3.4 Bayesian Inference -- 4 Results -- 5 Conclusions -- References -- Social Lag in the Municipalities of the State of Guerrero, México -- 1 Introduction -- 2 Methodology -- 2.1 CONEVAL -- 2.2 Clustering -- 3 Application -- 4 Results -- 4.1 CONEVAL -- 4.2 Cluster -- 4.3 Comparison -- 5 Conclusions -- References -- Challenges in Performing the Quick Counts of the National Electoral Institute in Mexico -- 1 Introduction -- 2 The Model -- 3 The Code -- 3.1 The Functional Code -- 3.2 First Idea: Use the Sample Design -- 3.3 Second Idea: Reach Out to the Community -- 4 Concluding Remarks -- References -- Sampling Design and Poststratification to Correct Lack of Information in Bayesian Quick Counts -- 1 Introduction -- 2 Model and Specifications -- 3 Sample Design -- 4 Incomplete Sample Estimation -- 4.1 General Strategy -- 4.2 Poststratification -- 4.3 Credibility Level Correction -- 5 Election Day -- 6 Conclusions.
References -- Maximum Likelihood Estimation for a Markov-Modulated Jump-Diffusion Model -- 1 Introduction -- 2 Markov-Modulated Jump-Diffusion Model -- 3 Methodology for the Maximum Likelihood Estimators -- 3.1 Identify the Jumps of the MJP -- 3.2 Estimate the Distribution of the Jumps -- 3.3 Estimate the Coefficients of the GBM -- 3.4 MLE of Q -- 4 Simulation Study -- 5 Real Data -- 6 Conclusions -- References -- Estimating the Composition of the Chamber of Deputies in the Quick Count for the 2021 Federal Election in Mexico -- 1 Introduction -- 1.1 The Quick Count -- 2 The Chamber of Deputies -- 2.1 Conformation -- 3 Estimation -- 3.1 Sampling Design -- 3.2 Bootstrap Estimation -- 4 Incomplete Samples -- 4.1 Multiple Imputation -- 5 Election Day: June 6, 2021 -- 5.1 Election Day Strategy -- 5.2 Arrival of Information -- 5.3 Estimations -- 6 Discussion -- A Appendix: Transforming Votes into Seats in the Deputy Chamber -- A.1 Relative Majority -- A.2 Voting Types Considered in the Law -- A.3 Proportional Representation -- References -- Bayesian Analysis of Homicide Rates in Mexico from 2000 to 2012 -- 1 Introduction -- 2 Literature Review -- 3 Data -- 4 Model Specification -- 5 Analysis and Results -- 6 Concluding Remarks -- References -- Index.
Record Nr. UNINA-9910633937503321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Interdisciplinary statistics in Mexico : AME virtual meeting, September 10-11, 2020, and 34 FNE, Acatlán, Mexico, September 22-24, 2021 / / Isadora Antoniano-Villalobos [and three others] editors
Interdisciplinary statistics in Mexico : AME virtual meeting, September 10-11, 2020, and 34 FNE, Acatlán, Mexico, September 22-24, 2021 / / Isadora Antoniano-Villalobos [and three others] editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (234 pages)
Disciplina 519.5
Collana Springer proceedings in mathematics & statistics
Soggetto topico Mathematical statistics
Statistics
Estadística matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 3-031-12778-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- A Methodological Proposal to Model and Evaluate the Complexity of the Mexican Geo-Electoral System -- 1 Introduction -- 2 Methodological Framework -- 2.1 Conceptualization of Electoral Complexity -- 2.2 Statistical Indicators for Quantifying Electoral Complexity -- 2.3 Data Transformation, Electoral Complexity Indices, and Stratification -- 3 ECI Construction and Stratification -- 3.1 Exploratory Analysis and PCA Implementation -- 3.2 Clustering Analysis and Stratification with K-Means -- 4 Electoral Complexity Ranking and Stratification Results -- 5 Conclusions -- References -- A Spatial Analysis of Drug Dealing in Mexico City -- 1 Introduction -- 2 Background -- 2.1 Drug Dealing as Part of Organized Crime -- 2.2 Research Approaches -- 3 Methodology -- 4 Results -- 4.1 Univariate Global and Local Moran's I -- 4.2 Bivariate Global and Local Moran's I -- 5 Discussion and Conclusions -- References -- Bayesian Hierarchical Multinomial Modeling of the 2021 Mexican Election Outcomes with Censored Samples -- 1 Introduction -- 2 Background -- 2.1 Estimation Methods in the 2021 Quick Count -- 2.2 NBM Model Background -- 2.3 The Bias Problem -- 3 The NBM Model -- 3.1 Specification -- 3.2 Consistency of Our Modeling Assumptions -- 3.3 Fitting Procedure -- 3.4 Model Adequacy -- 4 The 2021 Mexican Elections -- 4.1 Data and Sample Design -- 4.2 Results -- 5 Conclusions and Future Work -- References -- Assessing Hospitalization for SARS-CoV-2 Confirmed Cases by a Cross-Entropy Weighted Ensemble Classifier -- 1 Introduction -- 2 Material -- 2.1 Dataset Description -- 2.2 Data Analysis -- 3 Cross-entropy Weighted Ensemble Classifier -- 4 Results -- 5 Discussion -- 6 Conclusion -- References -- Emotion Analysis to Identify Risk of Committing Suicide Using Statistical Learning -- 1 Background -- 2 Materials and Methods.
2.1 Emotion Mining -- 2.2 Statistical Learning Methods -- 2.3 Training and Test Datasets -- 3 Results -- 3.1 Supervised Models -- 3.2 Unsupervised Model -- 3.3 Model Comparison -- 3.4 Testing the Models with a New Test Set: COVID-19 -- 4 Conclusions -- References -- Characterizing Groups Using Latent Class Mixed Models: Antiretroviral Treatment Adherence Analysis -- 1 Introduction -- 2 Materials -- 2.1 Population -- 2.2 Data Collection -- 2.3 ART Adherence Definition -- 3 Latent Class Mixture Models -- 4 Results -- 4.1 Latent Classes from ART Adherence -- 4.2 Latent Classes for Bivariate Response of CD4/CD8 Ratio and CD4+T Within Groups of Adherence -- 5 Discussion and Conclusions -- References -- A Dynamic Model for Analyzing the Public Health Policy of the Mexican Government During the COVID-19 Pandemic -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 SIRD Model -- 3.2 Transmission Rate Model -- 3.3 Recovery Rate Estimation -- 3.4 Bayesian Inference -- 4 Results -- 5 Conclusions -- References -- Social Lag in the Municipalities of the State of Guerrero, México -- 1 Introduction -- 2 Methodology -- 2.1 CONEVAL -- 2.2 Clustering -- 3 Application -- 4 Results -- 4.1 CONEVAL -- 4.2 Cluster -- 4.3 Comparison -- 5 Conclusions -- References -- Challenges in Performing the Quick Counts of the National Electoral Institute in Mexico -- 1 Introduction -- 2 The Model -- 3 The Code -- 3.1 The Functional Code -- 3.2 First Idea: Use the Sample Design -- 3.3 Second Idea: Reach Out to the Community -- 4 Concluding Remarks -- References -- Sampling Design and Poststratification to Correct Lack of Information in Bayesian Quick Counts -- 1 Introduction -- 2 Model and Specifications -- 3 Sample Design -- 4 Incomplete Sample Estimation -- 4.1 General Strategy -- 4.2 Poststratification -- 4.3 Credibility Level Correction -- 5 Election Day -- 6 Conclusions.
References -- Maximum Likelihood Estimation for a Markov-Modulated Jump-Diffusion Model -- 1 Introduction -- 2 Markov-Modulated Jump-Diffusion Model -- 3 Methodology for the Maximum Likelihood Estimators -- 3.1 Identify the Jumps of the MJP -- 3.2 Estimate the Distribution of the Jumps -- 3.3 Estimate the Coefficients of the GBM -- 3.4 MLE of Q -- 4 Simulation Study -- 5 Real Data -- 6 Conclusions -- References -- Estimating the Composition of the Chamber of Deputies in the Quick Count for the 2021 Federal Election in Mexico -- 1 Introduction -- 1.1 The Quick Count -- 2 The Chamber of Deputies -- 2.1 Conformation -- 3 Estimation -- 3.1 Sampling Design -- 3.2 Bootstrap Estimation -- 4 Incomplete Samples -- 4.1 Multiple Imputation -- 5 Election Day: June 6, 2021 -- 5.1 Election Day Strategy -- 5.2 Arrival of Information -- 5.3 Estimations -- 6 Discussion -- A Appendix: Transforming Votes into Seats in the Deputy Chamber -- A.1 Relative Majority -- A.2 Voting Types Considered in the Law -- A.3 Proportional Representation -- References -- Bayesian Analysis of Homicide Rates in Mexico from 2000 to 2012 -- 1 Introduction -- 2 Literature Review -- 3 Data -- 4 Model Specification -- 5 Analysis and Results -- 6 Concluding Remarks -- References -- Index.
Record Nr. UNISA-996499866703316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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
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
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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
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