Foundation of Computational Statistics, Series of Famous Mathematical Books Abroad, Facsimile Edition, 10

Author: Jintai
Publisher:
Publish Date: 2006-01-01
Features: Intensive computational methods have been widely used in statistical inference and exploratory data analysis. Computational statistics methods include resampling of datasets, classification, and multiple transformations, which may utilize randomly generated artificial data. The application of these methods requires advanced techniques in numerical analysis. Therefore, computational statistics and statistical computing methods are closely related. This book elaborates on various methods of computational statistics and some applications of intensive computational methods in density estimation, confirmation of data structures, and model building. Although this book does not specifically discuss statistical computing methods, it comprehensively covers numerical techniques in data transformation, function approximation, and data optimization in the context of statistical methods. The book provides exercises, some of which include solutions. While assuming readers are familiar with probability theory and statistics, the book also reviews basic methods of statistical inference, making it largely self-contained. This book can serve as a textbook or supplementary text for modern statistics courses for senior undergraduate or graduate students, or as a reference book for statisticians using intensive computational methods. The author is a professor in the Department of Computational Statistics at George Mason University, a member of the American Statistical Association (ASA) and the International Statistical Society, holding multiple national offices in ASA. He is the associate editor of ASA journals and serves as an editor for other journals in statistics and computing. He is also the author of "Random Number Generation, Monte Carlo Methods, and Numerical Linear Algebra in Statistics."