1st Edition

Analysis of Mixed Data Methods & Applications

    262 Pages
    by Chapman & Hall

    262 Pages 293 B/W Illustrations
    by Chapman & Hall

    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.

    Analysis of Mixed Data: An Overview. Combining Univariate and Multivariate Random Forests for Enhancing Predictions of Mixed Outcomes. Joint Tests for Mixed Traits in Genetic Association Studies. Bias in Factor Score Regression and a Simple Solution. Joint Modeling of Mixed Count and Continuous Longitudinal Data. Factorization and Latent Variable Models for Joint Analysis of Binary and Continuous Outcomes. Regression Models for Analyzing Clustered Binary and Continuous Outcomes under the Assumption of Exchangeability. Random Effects Models for Joint Analysis of Repeatedly Measured Discrete and Continuous Outcomes. Hierarchical Modeling of Endpoints of Different Types with Generalized Linear Mixed Models. Joint Analysis of Mixed Discrete and Continuous Outcomes via Copula Models. Analysis of Mixed Outcomes in Econometrics: Applications in Health Economics. Sparse Bayesian Modeling of Mixed Econometric Data Using Data Augmentation. Bayesian Methods for the Analysis of Mixed Categorical and Continuous (Incomplete) Data.


    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 [email protected]

    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 Scient