Hierarchical Quantile Modeling

This book offers a concise and comprehensive introduction to Hierarchical Quantile Modeling, a modern statistical methodology that extends traditional hierarchical models and quantile regression techniques to analyze complex data structures often found in fields like biology, economics, and education. Unlike classic models, Hierarchical Quantile Modeling accommodates heteroscedasticity and nonparametric relationships, allowing for a detailed study of the entire conditional distribution of a response variable.

The book is structured in four parts: an introduction to hierarchical modeling, a detailed look at quantile regression, an in-depth exploration of Hierarchical Quantile Modeling, and practical applications using real-world hierarchical, repeated, and clustered data. Drawing on the author’s decade-long experience in research and teaching, this guide is ideal for graduate students, researchers, and practitioners. It includes examples and software guidance using R, S-plus, SAS, and SPSS, making it a valuable resource for anyone interested in advanced statistical analysis.

Professor TIAN Maozai is Vice Director of Center for Applied Statistics, Renmin University of China. His research covers a large range of topics in mathematics and statistics, such as quantile regression, hierarchical models, hierarchical- quantile regression modeling, big data modeling, adaptive smoothing, Bayesian statistical inference, computer intensive methods, etc.