• Methods and Applications of Longitudinal Data Analysis (Hardback) 9780128013427
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Methods and Applications of Longitudinal Data Analysis

From classical to the most advanced longitudinal methods, a complete guide, empirically illustrated

Xian Liu (Author)

9780128013427, Elsevier Science

Hardback, published 8 September 2015

530 pages
23.4 x 19 x 3.1 cm, 1.11 kg

Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include:

  • descriptive methods for delineating trends over time
  • linear mixed regression models with both fixed and random effects
  • covariance pattern models on correlated errors
  • generalized estimating equations
  • nonlinear regression models for categorical repeated measurements
  • techniques for analyzing longitudinal data with non-ignorable missing observations

Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data.

Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists.

1 Introduction2 Traditional Methods of Longitudinal Data Analysis3 Linear Mixed-effects Models4 Restricted Maximum Likelihood and Inference of Random Effects in Linear Mixed Models5 Patterns of Residual Covariance Structure6 Residual and Influence Diagnostics7 Special Topics on Linear Mixed Models 8 Generalized Linear Mixed Models on Nonlinear Longitudinal Data9 Generalized Estimating Equations Models (GEEs)10 Mixed-effects Regression Model for Binary Longitudinal Data11 Mixed-effects Multinomial Logit Model for Nominal Outcomes12 Longitudinal Transition Models for Categorical Response Data13 Latent Growth, Latent Growth Mixture, and Group-based Models14 Methods for Handling Missing DataAppendix A: Orthogonal PolynomialsAppendix B: The Delta Method Appendix C: Quasi-likelihood Functions and PropertiesAppendix D: Model Specification and SAS Program for Random Coefficient Multinomial Logit Model on Health States among Older AmericansReferencesSubject Index

Subject Areas: Applied mathematics [PBW], Probability & statistics [PBT]
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