Mixed Effects Models for Complex Data

Released on by Lang Wu
preview-18

  • Mixed Effects Models for Complex Data Book Detail

  • Author : Lang Wu
  • Release Date : 2009-11-11
  • Publisher : CRC Press
  • Genre : Mathematics
  • Pages : 431
  • ISBN 13 : 9781420074086
  • File Size : 29,29 MB
DOWNLOAD BOOK

Mixed Effects Models for Complex Data by Lang Wu PDF Summary

Book Description: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Disclaimer: www.yourbookbest.com does not own Mixed Effects Models for Complex Data books pdf, neither created or scanned. We just provide the link that is already available on the internet, public domain and in Google Drive. If any way it violates the law or has any issues, then kindly mail us via contact us page to request the removal of the link.

Mixed Effects Models for Complex Data

Mixed Effects Models for Complex Data

File Size : 99,99 MB
Total View : 6278 Views
DOWNLOAD

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or i

Mixed Models

Mixed Models

File Size : 98,98 MB
Total View : 6580 Views
DOWNLOAD

Praise for the First Edition “This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend includi

Linear Mixed Models

Linear Mixed Models

File Size : 1,1 MB
Total View : 5562 Views
DOWNLOAD

Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM