Applied Categorical and Count Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)

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Wan Tang is a Clinical Associate Professor in the Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine. Dr. Tang’s research interests include longitudinal data analysis, missing data modeling, structural equation models, causal inference, and nonparametric smoothing methods.

Hua He, Ph.D. is an Associate Professor in Biostatistics at the Department of Epidemiology at Tulane University School of Public Health and Tropical Medicine. Dr. He is a highly experienced biostatistician with expertise in longitudinal data analysis, structural equation models, potential outcome based causal inference, distribution-free models, ROC analysis and their applications to observational studies, and randomized controlled trials across a range of disciplines, especially in the behavioral and social sciences. She has co-authored a series of publications in peer-reviewed journals, one textbook on categorical data analysis and co-edited a book on statistical causal inference and their applications in public health research.

Xin Tu (Ph.D.) is a Professor in the Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, UCSD. Dr. Tu is well versed in statistical methods and their applications to a range of disciplines, particularly within the fields of biomedical, behavioral and social sciences. He has co-authored over 200 peer-reviewed publications, two textbooks on categorical data and applied U-statistics, and co-edited books on modern clinical trials and social network data analysis. He has done important work in the areas of longitudinal data analysis, U-statistics, survival analysis with interval censoring and truncation, and pooled testing, and has successfully applied his novel development to addressing important methodological problems in HIV/AIDS, mental health and psychosocial research.