Introduction to Mathematical Statistics
Introduction to Mathematical Statistics
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Summary
Introduction to Mathematical Statistics, Seventh Edition, provides students with a comprehensive introduction to mathematical statistics. Continuing its proven approach, the Seventh Edition has been updated with new examples, exercises, and content for an even stronger presentation of the material.
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Introduction to Mathematical Statistics by Robert Hogg
Introduction to Mathematical Statistics, Seventh Edition, offers a proven approach designed to provide you with an excellent foundation in mathematical statistics. Ample examples and exercises throughout the text illustrate concepts to help you gain a solid understanding of the material.
Dr. Regina Liu is currently Distinguished Professor of Statistics at Rutgers University, USA. She received her Ph.D. from Columbia University at New York. She has published extensively in a broad range of research areas, including nonparametric statistics, data depth, robust statistics, resampling techniques, text mining, fusion learning, statistical quality control, and aviation risk management. She has served on the editorial board of several statistical journals, including The Annals of Statistics, Journal of American Statistical Association, and Journal of Multivariate Analysis. She is the recipient of the 2011 Stieltjes Professor, Thomas Stieltjes Institute for Mathematics, the Netherlands. She has been elected fellow of American Statistical Association, Institute of Mathematical Statistics, and International Statistical Institute. Dr. Joseph McKean is Professor of Statistics at Western Michigan University. He received his PhD in Statistics in 1975 from the Pennsylvania State University under the direction of Professor T.P. Hettmansperger. He has held several visiting research professorships at University of New South Wales. In 1999, he was elected as a fellow of the American Statistical Association. In 1994, he received the Distinguished Faculty Scholar Award from Western Michigan University. He served as Chair of the Nonparametric Section of the American Statistical Association during 2002. Dr. McKean has served on the editorial board of several statistical journals, including the Journal of the American Statistical Association, the Journal of Statistical Computation and Simulation, and the Journal of Nonparametric Statistics.
Dr. McKean has published extensively on robust rank-based procedures for linear models. These include papers on the theory for robust estimation and testing, the geometry of robust procedures, and the small sample properties of robust inference. He has worked with general robust estimates, bounded inuence estimates, and high breakdown estimates. He has co-authored a series of papers on diagnostic procedures for robust estimation. Besides robust procedures, Dr. McKean has published in the areas of generalized linear models, nonparametric statistics and time series analyses. He has recently published articles on rank-based procedures for nonlinear, mixed, and GEE models. He is a co-author (with T.P. Hettmansperger) of the monograph Robust Nonparametric Statistical Methods. He has worked on algorithm development and software for these procedures including the R package Rfit and has co-authored (with J.D. Kloke) the book Nonparametric Statistical Methods Using R. His current investigations include rank-based algorithms for Big Data, rank-based Bayesian methods for linear and mixed models, visualization techniques, and robust methods for linear models with autoregressive errors. Dr. McKean has served as the dissertation advisor for twenty-six PhD students. He is a co-author, (with R.V. Hogg), of the text, Introduction to Mathematical Statistics.
Dr. McKean has published extensively on robust rank-based procedures for linear models. These include papers on the theory for robust estimation and testing, the geometry of robust procedures, and the small sample properties of robust inference. He has worked with general robust estimates, bounded inuence estimates, and high breakdown estimates. He has co-authored a series of papers on diagnostic procedures for robust estimation. Besides robust procedures, Dr. McKean has published in the areas of generalized linear models, nonparametric statistics and time series analyses. He has recently published articles on rank-based procedures for nonlinear, mixed, and GEE models. He is a co-author (with T.P. Hettmansperger) of the monograph Robust Nonparametric Statistical Methods. He has worked on algorithm development and software for these procedures including the R package Rfit and has co-authored (with J.D. Kloke) the book Nonparametric Statistical Methods Using R. His current investigations include rank-based algorithms for Big Data, rank-based Bayesian methods for linear and mixed models, visualization techniques, and robust methods for linear models with autoregressive errors. Dr. McKean has served as the dissertation advisor for twenty-six PhD students. He is a co-author, (with R.V. Hogg), of the text, Introduction to Mathematical Statistics.
| SKU | Unavailable |
| ISBN 13 | 9780321795434 |
| ISBN 10 | 0321795431 |
| Title | Introduction to Mathematical Statistics |
| Author | Robert Hogg |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Pearson Education (US) |
| Year published | 2011-12-23 |
| Number of pages | 704 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |