Features
Provides a comprehensive, unified, and timely account of major advances
Includes contributions from major researchers on mixed data analysis
Presents a synthesis and development of future research directions

Summary
A comprehensive source on mixed data analysis, Analysis of Mixed Data: Methods & Applications summarizes the fundamental developments in the field. Case studies are used extensively throughout the book to illustrate interesting applications from economics, medicine and health, marketing, and genetics.

Carefully edited for smooth readability and seamless transitions between chapters
All chapters follow a common structure, with an introduction and a concluding summary, and include illustrative examples from real-life case studies in developmental toxicology, economics, medicine and health, marketing, and genetics
An introductory chapter provides a "wide angle" introductory overview and comprehensive survey of mixed data analysis

Blending theory and methodology, this book illustrates concepts via data from different disciplines. Analysis of Mixed Data: Methods & Applications traces important developments, collates basic results, presents terminology and methodologies, and gives an overview of statistical research applications. It is a valuable resource to methodologically interested as well as subject matter-motivated researchers in many disciplines

Table of Contents
Analysis of mixed data: An overview
Alexander R. de Leon and Keumhee Carrière Chough
Introduction
Early developments in mixed data analysis
Joint analysis of mixed outcomes
Highlights of book

Combining univariate and multivariate random forests for enhancing predictions of mixed outcomes
Abdessamad Dine, Denis Larocque, and François Bellavance
Introduction
Predictions from univariate and multivariate random forests
Simulation study
Discussion

Joint tests for mixed traits in genetic association studies
Minjung Kwak, Gang Zheng, and Colin O. Wu
Introduction
Analysis of binary or quantitative traits
Joint analysis of mixed traits
Application
Discussion

Bias in factor score regression and a simple solution
Takahiro Hoshino and Peter M. Bentler
Introduction
Model
Bias due to estimated factor scores: Factor analysis model
Proposed estimation method
Simulation studies
Application
Theoretical details
Discussion

Joint modeling of mixed count and continuous longitudinal data
Jian Kang and Ying Yang
Introduction
Complete data model
Handling missing data problem
Application
Discussion

Factorization and latent variable models for joint analysis of binary and continuous outcomes
Armando Teixeira Pinto and Jaroslaw Harezlak
Introduction
Clinical trial on bare-metal and drug-eluting stents
Separate analyses
Factorization models for binary and continuous outcomes
Latent variable models for binary and continuous outcomes
Software
Discussion

Regression models for analyzing clustered binary and continuous outcomes under the assumption of exchangeability
E. Olusegun George, Dale Bowman, and Qi An
Introduction
Distribution theory and likelihood representation
Parametric models
Application to DEHP data
Litter-specific joint quantitative risk assessment
Discussion

Random effects models for joint analysis of repeatedly measured discrete and continuous outcomes
Ralitza Gueorguieva
Introduction
Models
Estimation and inference
Applications
Discussion

Hierarchical modeling of endpoints of different types with generalized linear mixed models
Christel Faes
Introduction
Multivariate multi-level models
Special cases
Likelihood inference
Applications
Discussion

Joint analysis of mixed discrete and continuous outcomes via copula models
Beilei Wu, Alexander R. de Leon, and Niroshan Withanage
Introduction
Joint models via copulas
Associations
Likelihood estimation
Analysis of ethylene glycol toxicity data
Discussion

Analysis of mixed outcomes in econometrics: Applications in health economics
David M. Zimmer
Introduction
Random effects models
Copula models
Application to drug spending and health status
Application to nondrug spending and drug usage
Discussion

Sparse Bayesian modeling of mixed econometric data using data augmentation
Helga Wagner and Regina Tüchler
Introduction
Model specification
Logit-normal model
Modeling material deprivation and household income
Estimating consumer behavior from panel data
Discussion

Bayesian methods for the analysis of mixed categorical and continuous (incomplete) data
Michael J. Daniels and Jeremy T. Gaskins
Introduction
Examples
Characterizing dependence
(Informative) Priors
Incomplete responses
General computational issues
Analysis of examples
Discussion