MARC details
| 000 -LEADER |
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| fixed length control field |
06674nam a2200169 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781439813263 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
003.35133 BER |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Berridge, Damon; Crouchley, Robert |
| 245 ## - TITLE STATEMENT |
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| Title |
Multivariate generalized linear mixed models using R |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
London |
| Name of publisher, distributor, etc. |
CRC Press |
| Date of publication, distribution, etc. |
2015 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxi, 280 p. |
| Other physical details |
24 cm; Hard Cover |
| 500 ## - GENERAL NOTE |
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| General note |
Alpha Invoice.2231- 11th Feb 16 Rs.2,995.00/- |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Introduction<br/><br/>Generalized Linear Models for Continuous/Interval Scale Data <br/>Introduction <br/>Continuous/interval scale data <br/>Simple and multiple linear regression models <br/>Checking assumptions in linear regression models <br/>Likelihood: multiple linear regression<br/>Comparing model likelihoods <br/>Application of a multiple linear regression model<br/><br/>Generalized Linear Models for Other Types of Data <br/>Binary data<br/>Ordinal data<br/>Count data<br/><br/>Family of Generalized Linear Models <br/>Introduction <br/>The linear model <br/>Binary response models <br/>Poisson model<br/>Likelihood<br/><br/>Mixed Models for Continuous/Interval Scale Data <br/>Introduction <br/>Linear mixed model <br/>The intraclass correlation coefficient <br/>Parameter estimation by maximum likelihood <br/>Regression with level-two effects <br/>Two-level random intercept models <br/>General two-level models including random intercepts <br/>Likelihood<br/>Residuals <br/>Checking assumptions in mixed models <br/>Comparing model likelihoods <br/>Application of a two-level linear model <br/>Two-level growth models<br/>Likelihood<br/>Example on linear growth models<br/><br/>Mixed Models for Binary Data <br/>Introduction <br/>The two-level logistic model <br/>General two-level logistic models <br/>Intraclass correlation coefficient <br/>Likelihood<br/>Example on binary data<br/><br/>Mixed Models for Ordinal Data <br/>Introduction <br/>The two-level ordered logit model <br/>Likelihood<br/>Example on mixed models for ordered data<br/><br/>Mixed Models for Count Data <br/>Introduction <br/>The two-level Poisson model <br/>Likelihood<br/>Example on mixed models for count data<br/><br/>Family of Two-Level Generalized Linear Models <br/>Introduction <br/>The mixed linear model <br/>Mixed binary response models <br/>Mixed Poisson model <br/>Likelihood<br/><br/>Three-Level Generalized Linear Models <br/>Introduction <br/>Three-level random intercept models <br/>Three-level generalized linear models <br/>Linear models <br/>Binary response models <br/>Likelihood<br/>Example on three-level generalized linear models<br/><br/>Models for Multivariate Data <br/>Introduction <br/>Multivariate two-level generalized linear model <br/>Bivariate Poisson model: Example <br/>Bivariate ordered response model: Example <br/>Bivariate linear-probit model: Example <br/>Multivariate two-level generalized linear model likelihood<br/><br/>Models for Duration and Event History Data <br/>Introduction<br/>Duration data in discrete time<br/>Renewal data<br/>Competing risk data<br/><br/>Stayers, Non-Susceptibles, and Endpoints <br/>Introduction <br/>Mover-stayer model <br/>Likelihood with mover-stayer model<br/>Example 1: Stayers in Poisson data <br/>Example 2: Stayers in binary data<br/><br/>Handling Initial Conditions/State Dependence in Binary Data <br/>Introduction to key issues: heterogeneity, state dependence and non-stationarity <br/>Motivational example <br/>Random effects model <br/>Initial conditions problem <br/>Initial treatment of initial conditions problem <br/>Example: Depression data <br/>Classical conditional analysis <br/>Classical conditional model: Depression example <br/>Conditioning on initial response but allowing random effect u0j to be dependent on zj<br/>Wooldridge conditional model: Depression example <br/>Modeling the initial conditions <br/>Same random effect in the initial response and subsequent response models with a common scale parameter <br/>Joint analysis with a common random effect: Depression example <br/>Same random effect in models of the initial response and subsequent responses but with different scale parameters<br/>Joint analysis with a common random effect (different scale parameters): Depression example <br/>Different random effects in models of the initial response and subsequent responses<br/>Different random effects: Depression example <br/>Embedding the Wooldridge approach in joint models for the initial response and subsequent responses <br/>Joint model plus the Wooldridge approach: Depression example <br/>Other link functions<br/><br/>Incidental Parameters: An Empirical Comparison of Fixed Effects and Random Effects Models<br/>Introduction <br/>Fixed effects treatment of the two-level linear model <br/>Dummy variable specification of the fixed effects model <br/>Empirical comparison of two-level fixed effects and random effects estimators <br/>Implicit fixed effects estimator <br/>Random effects models <br/>Comparing two-level fixed effects and random effects models <br/>Fixed effects treatment of the three-level linear model<br/><br/>Appendix A: SabreR Installation, SabreR Commands, Quadrature, Estimation, Endogenous Effects<br/>Appendix B: Introduction to R for Sabre<br/><br/><br/>Bibliography<br/><br/>Exercises appear at the end of most chapters. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R.<br/><br/>A Unified Framework for a Broad Class of Models <br/>The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples.<br/><br/>Improve Your Longitudinal Study<br/>In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website.<br/><br/>Share this Title<br/><br/>Recommend to <br/>Librarian<br/>Related Titles<br/>1 of 8<br/> <br/>Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models<br/> |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Statistics |
| -- |
Multivariate analysis |
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Social sciences--Research--Data processing - Using R |
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Social sciences--Research--Mathematical models |
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Social sciences--Research--Statistical methods |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
Books |
