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.
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.