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Bayesian models for categorical data [[electronic resource] /] / Peter Congdon
| Bayesian models for categorical data [[electronic resource] /] / Peter Congdon |
| Autore | Congdon P |
| Pubbl/distr/stampa | Chichester ; ; New York, : Wiley, c2005 |
| Descrizione fisica | 1 online resource (448 p.) |
| Disciplina | 519.542 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Monte Carlo method Markov processes Multivariate analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-28770-5
9786610287703 0-470-09239-4 0-470-09238-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Models for Categorical Data; Contents; Preface; Chapter 1 Principles of Bayesian Inference; 1.1 Bayesian updating; 1.2 MCMC techniques; 1.3 The basis for MCMC; 1.4 MCMC sampling algorithms; 1.5 MCMC convergence; 1.6 Competing models; 1.7 Setting priors; 1.8 The normal linear model and generalized linear models; 1.9 Data augmentation; 1.10 Identifiability; 1.11 Robustness and sensitivity; 1.12 Chapter themes; References; Chapter 2 Model Comparison and Choice; 2.1 Introduction: formal methods, predictive methods and penalized deviance criteria; 2.2 Formal Bayes model choice
2.3 Marginal likelihood and Bayes factor approximations2.4 Predictive model choice and checking; 2.5 Posterior predictive checks; 2.6 Out-of-sample cross-validation; 2.7 Penalized deviances from a Bayes perspective; 2.8 Multimodel perspectives via parallel sampling; 2.9 Model probability estimates from parallel sampling; 2.10 Worked example; References; Chapter 3 Regression for Metric Outcomes; 3.1 Introduction: priors for the linear regression model; 3.2 Regression model choice and averaging based on predictor selection; 3.3 Robust regression methods: models for outliers 3.4 Robust regression methods: models for skewness and heteroscedasticity3.5 Robustness via discrete mixture models; 3.5.1 Complete data representation; 3.5.2 Identification issues; 3.5.3 Dirichlet process mixture models; 3.6 Non-linear regression effects via splines and other basis functions; 3.6.1 Penalized random effects for spline coefficients; 3.6.2 Basis function regression; 3.6.3 Special spline functions; 3.7 Dynamic linear models and their application in non-parametric regression; 3.7.1 Some common forms of DLM; 3.7.2 Robust errors; 3.7.3 General additive models 3.7.4 Alternative smoothness priorsExercises; References; Chapter 4 Models for Binary and Count Outcomes; 4.1 Introduction: discrete model likelihoods vs. data augmentation; 4.1.1 Count data; 4.1.2 Binomial and binary data; 4.2 Estimation by data augmentation: the Albert-Chib method; 4.2.1 Other augmented data methods; 4.3 Model assessment: outlier detection and model checks; 4.3.1 Model assessment: predictive model selection and checks; 4.4 Predictor selection in binary and count regression; 4.5 Contingency tables 4.6 Semi-parametric and general additive models for binomial and count responses4.6.1 Robust and adaptive non-parametric regression; 4.6.2 Other approaches to non-linearity; Exercises; References; Chapter 5 Further Questions in Binomial and Count Regression; 5.1 Generalizing the Poisson and binomial: overdispersion and robustness; 5.2 Continuous mixture models; 5.2.1 Modified exponential families; 5.3 Discrete mixtures; 5.4 Hurdle and zero-inflated models; 5.5 Modelling the link function; 5.5.1 Discrete (DPP mixture); 5.5.2 Parametric link transformations 5.5.3 Beta mixture on cumulative densities |
| Record Nr. | UNINA-9910145045003321 |
Congdon P
|
||
| Chichester ; ; New York, : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian models for categorical data [[electronic resource] /] / Peter Congdon
| Bayesian models for categorical data [[electronic resource] /] / Peter Congdon |
| Autore | Congdon P |
| Pubbl/distr/stampa | Chichester ; ; New York, : Wiley, c2005 |
| Descrizione fisica | 1 online resource (448 p.) |
| Disciplina | 519.542 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Monte Carlo method Markov processes Multivariate analysis |
| ISBN |
1-280-28770-5
9786610287703 0-470-09239-4 0-470-09238-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Models for Categorical Data; Contents; Preface; Chapter 1 Principles of Bayesian Inference; 1.1 Bayesian updating; 1.2 MCMC techniques; 1.3 The basis for MCMC; 1.4 MCMC sampling algorithms; 1.5 MCMC convergence; 1.6 Competing models; 1.7 Setting priors; 1.8 The normal linear model and generalized linear models; 1.9 Data augmentation; 1.10 Identifiability; 1.11 Robustness and sensitivity; 1.12 Chapter themes; References; Chapter 2 Model Comparison and Choice; 2.1 Introduction: formal methods, predictive methods and penalized deviance criteria; 2.2 Formal Bayes model choice
2.3 Marginal likelihood and Bayes factor approximations2.4 Predictive model choice and checking; 2.5 Posterior predictive checks; 2.6 Out-of-sample cross-validation; 2.7 Penalized deviances from a Bayes perspective; 2.8 Multimodel perspectives via parallel sampling; 2.9 Model probability estimates from parallel sampling; 2.10 Worked example; References; Chapter 3 Regression for Metric Outcomes; 3.1 Introduction: priors for the linear regression model; 3.2 Regression model choice and averaging based on predictor selection; 3.3 Robust regression methods: models for outliers 3.4 Robust regression methods: models for skewness and heteroscedasticity3.5 Robustness via discrete mixture models; 3.5.1 Complete data representation; 3.5.2 Identification issues; 3.5.3 Dirichlet process mixture models; 3.6 Non-linear regression effects via splines and other basis functions; 3.6.1 Penalized random effects for spline coefficients; 3.6.2 Basis function regression; 3.6.3 Special spline functions; 3.7 Dynamic linear models and their application in non-parametric regression; 3.7.1 Some common forms of DLM; 3.7.2 Robust errors; 3.7.3 General additive models 3.7.4 Alternative smoothness priorsExercises; References; Chapter 4 Models for Binary and Count Outcomes; 4.1 Introduction: discrete model likelihoods vs. data augmentation; 4.1.1 Count data; 4.1.2 Binomial and binary data; 4.2 Estimation by data augmentation: the Albert-Chib method; 4.2.1 Other augmented data methods; 4.3 Model assessment: outlier detection and model checks; 4.3.1 Model assessment: predictive model selection and checks; 4.4 Predictor selection in binary and count regression; 4.5 Contingency tables 4.6 Semi-parametric and general additive models for binomial and count responses4.6.1 Robust and adaptive non-parametric regression; 4.6.2 Other approaches to non-linearity; Exercises; References; Chapter 5 Further Questions in Binomial and Count Regression; 5.1 Generalizing the Poisson and binomial: overdispersion and robustness; 5.2 Continuous mixture models; 5.2.1 Modified exponential families; 5.3 Discrete mixtures; 5.4 Hurdle and zero-inflated models; 5.5 Modelling the link function; 5.5.1 Discrete (DPP mixture); 5.5.2 Parametric link transformations 5.5.3 Beta mixture on cumulative densities |
| Record Nr. | UNINA-9910830214403321 |
|
Congdon P
|
||
| Chichester ; ; New York, : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian models for categorical data / / Peter Congdon
| Bayesian models for categorical data / / Peter Congdon |
| Autore | Congdon P |
| Pubbl/distr/stampa | Chichester ; ; New York, : Wiley, c2005 |
| Descrizione fisica | 1 online resource (448 p.) |
| Disciplina | 519.5/42 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Monte Carlo method Markov processes Multivariate analysis |
| ISBN |
9786610287703
9781280287701 1280287705 9780470092392 0470092394 9780470092385 0470092386 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Models for Categorical Data; Contents; Preface; Chapter 1 Principles of Bayesian Inference; 1.1 Bayesian updating; 1.2 MCMC techniques; 1.3 The basis for MCMC; 1.4 MCMC sampling algorithms; 1.5 MCMC convergence; 1.6 Competing models; 1.7 Setting priors; 1.8 The normal linear model and generalized linear models; 1.9 Data augmentation; 1.10 Identifiability; 1.11 Robustness and sensitivity; 1.12 Chapter themes; References; Chapter 2 Model Comparison and Choice; 2.1 Introduction: formal methods, predictive methods and penalized deviance criteria; 2.2 Formal Bayes model choice
2.3 Marginal likelihood and Bayes factor approximations2.4 Predictive model choice and checking; 2.5 Posterior predictive checks; 2.6 Out-of-sample cross-validation; 2.7 Penalized deviances from a Bayes perspective; 2.8 Multimodel perspectives via parallel sampling; 2.9 Model probability estimates from parallel sampling; 2.10 Worked example; References; Chapter 3 Regression for Metric Outcomes; 3.1 Introduction: priors for the linear regression model; 3.2 Regression model choice and averaging based on predictor selection; 3.3 Robust regression methods: models for outliers 3.4 Robust regression methods: models for skewness and heteroscedasticity3.5 Robustness via discrete mixture models; 3.5.1 Complete data representation; 3.5.2 Identification issues; 3.5.3 Dirichlet process mixture models; 3.6 Non-linear regression effects via splines and other basis functions; 3.6.1 Penalized random effects for spline coefficients; 3.6.2 Basis function regression; 3.6.3 Special spline functions; 3.7 Dynamic linear models and their application in non-parametric regression; 3.7.1 Some common forms of DLM; 3.7.2 Robust errors; 3.7.3 General additive models 3.7.4 Alternative smoothness priorsExercises; References; Chapter 4 Models for Binary and Count Outcomes; 4.1 Introduction: discrete model likelihoods vs. data augmentation; 4.1.1 Count data; 4.1.2 Binomial and binary data; 4.2 Estimation by data augmentation: the Albert-Chib method; 4.2.1 Other augmented data methods; 4.3 Model assessment: outlier detection and model checks; 4.3.1 Model assessment: predictive model selection and checks; 4.4 Predictor selection in binary and count regression; 4.5 Contingency tables 4.6 Semi-parametric and general additive models for binomial and count responses4.6.1 Robust and adaptive non-parametric regression; 4.6.2 Other approaches to non-linearity; Exercises; References; Chapter 5 Further Questions in Binomial and Count Regression; 5.1 Generalizing the Poisson and binomial: overdispersion and robustness; 5.2 Continuous mixture models; 5.2.1 Modified exponential families; 5.3 Discrete mixtures; 5.4 Hurdle and zero-inflated models; 5.5 Modelling the link function; 5.5.1 Discrete (DPP mixture); 5.5.2 Parametric link transformations 5.5.3 Beta mixture on cumulative densities |
| Record Nr. | UNINA-9911019459403321 |
|
Congdon P
|
||
| Chichester ; ; New York, : Wiley, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian statistical modelling [[electronic resource] /] / Peter Congdon
| Bayesian statistical modelling [[electronic resource] /] / Peter Congdon |
| Autore | Congdon P |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2006 |
| Descrizione fisica | 1 online resource (597 p.) |
| Disciplina | 519.542 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico | Bayesian statistical decision theory |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-83897-3
9786610838974 0-470-03594-3 0-470-03593-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction : the Bayesian method, its benefits and implementation -- Bayesian model choice, comparison and checking -- The major densities and their application -- Normal linear regression, general linear models and log-linear models -- Hierarchical priors for pooling strength and overdispersed regression modelling -- Discrete mixture priors -- Multinomial and ordinal regression models -- Time series models -- Modelling spatial dependencies -- Nonlinear and nonparametric regression -- Multilevel and panel data models -- Latent variable and structural equation models for multivariate data -- Survival and event history analysis -- Missing data models -- Measurement error, seemingly unrelated regressions, and simultaneous eqations. |
| Record Nr. | UNINA-9910143735103321 |
|
Congdon P
|
||
| Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bayesian statistical modelling / / Peter Congdon
| Bayesian statistical modelling / / Peter Congdon |
| Autore | Congdon P |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2006 |
| Descrizione fisica | 1 online resource (597 p.) |
| Disciplina | 519.5/42 |
| Collana | Wiley series in probability and statistics |
| Soggetto topico | Bayesian statistical decision theory |
| ISBN |
9786610838974
9781280838972 1280838973 9780470035948 0470035943 9780470035931 0470035935 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction : the Bayesian method, its benefits and implementation -- Bayesian model choice, comparison and checking -- The major densities and their application -- Normal linear regression, general linear models and log-linear models -- Hierarchical priors for pooling strength and overdispersed regression modelling -- Discrete mixture priors -- Multinomial and ordinal regression models -- Time series models -- Modelling spatial dependencies -- Nonlinear and nonparametric regression -- Multilevel and panel data models -- Latent variable and structural equation models for multivariate data -- Survival and event history analysis -- Missing data models -- Measurement error, seemingly unrelated regressions, and simultaneous eqations. |
| Record Nr. | UNINA-9911019564103321 |
|
Congdon P
|
||
| Chichester, England ; ; Hoboken, NJ, : John Wiley & Sons, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||