Analysis of Mixed Data: Methods and Applications

Analysis of Mixed Data: Methods and Applications

By: Alexander R. De Leon (editor), Keumhee Carriere Chough (editor)Hardback

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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.

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About Author

Alexander R. de Leon is Associate Professor in the Department of Mathematics and Statistics at the University of Calgary. Originally from the Philippines, he obtained his BSc and MSc, both in Statistics, from the School of Statistics of the University of the Philippines. After a research studentship at Tokyo University of Science, he completed his PhD in Statistics in 2002 at the University of Alberta. His research interests include methods for analyzing correlated data, multivariate models and distances for mixed discrete and continuous outcomes, pseudo- and composite likelihood methods, copula modeling, assessment of diagnostic tests, statistical quality control, and statistical problems in medicine, particularly in ophthalmology. Alex can be reached at Keumhee Carriere Chough is Professor of Statistics in the Department of Mathematical and Statistical Sciences at the University of Alberta. After completing her BSc in Agriculture from Seoul National University, in Seoul, Korea, she earned her MSc from the University of Manitoba, and her PhD in Statistics from the University of Wisconsin-Madison in 1989. Since 1996, she has been with the Department of Mathematical and Statistical Sciences, University of Alberta, after stints as Assistant Professor at the University of Iowa (1990-1992) and University of Manitoba (1992-1996). She was also the Director of the Statistics Consulting Center at the University of Iowa (1990-1992). Her research interests include design and analysis for repeated measures data, missing data methods, high dimensional data analysis methods, multivariate methods, designs for clinical trials, item response data, variable selection methods, and survival analysis. As well, she specializes in such biostatistical methods as small area variation analysis techniques with applications to health care utilization. She has been a Health Scientist funded through the Alberta Heritage Foundation for Medical Research (1996-2011). She is a Fellow of the American Statistical Association, the Institute of Health Economics, and the Manitoba Centre for Health Policy. Keumhee can be reached at


Analysis of mixed data: An overview Alexander R. de Leon and Keumhee Carriere 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 Francois 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 Tuchler 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

Product Details

  • publication date: 30/01/2013
  • ISBN13: 9781439884713
  • Format: Hardback
  • Number Of Pages: 262
  • ID: 9781439884713
  • weight: 703
  • ISBN10: 1439884714

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