Nonparametric Inference
$212.54
- Hardcover
354 pages
- Release Date
6 August 2026
Summary
This book provides a comprehensive and balanced treatment of both classical and modern methods in nonparametric inference. It begins with foundational topics such as order statistics, ranks, and confidence intervals for medians and percentiles before progressing to distribution-free tests, robust estimators, regression quantiles and U-statistics. Advanced topics include nonparametric density and regression estimation, model diagnostics, empirical likelihood, and survival analysis, including n…
Book Details
| ISBN-13: | 9781032956138 |
|---|---|
| ISBN-10: | 1032956135 |
| Author: | Hira L. Koul, Anton Schick, Palaniappan Vellaisamy |
| Publisher: | Taylor & Francis Ltd |
| Imprint: | Chapman & Hall/CRC |
| Format: | Hardcover |
| Number of Pages: | 354 |
| Release Date: | 6 August 2026 |
| Weight: | 840g |
| Dimensions: | 178mm x 254mm |
| Series: | Chapman & Hall/CRC Texts in Statistical Science |
Hira L. Koul
Hira L. Koul
Secured his doctorate in statistics from the University of California, Berkeley in 1967. He joined the Department of Statistics and Probability, Michigan State University (MSU) on January 1, 1968. Since January 1, 2018, he has been Professor Emeritus at MSU, after serving there as a faculty member for 50 years. His areas of research include nonparametric inference, inference on short and long memory processes, time series analysis and survival analysis. He has published around 150 papers, several monographs and books and guided 35 doctoral theses. He is a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics and Past President of the International Indian Statistical Association. He was a recipient of a Humboldt Research Award for senior scientists in October 1995 and a Distinguished Faculty Award at MSU in 2005.
Anton Schick
Earned his doctorate in statistics from Michigan State University in 1983. He spent one year at Tufts University before joining the Department of Mathematical Sciences at Binghamton University in the fall of 1984. He retired as full professor on September 1, 2024 after forty years of service that included two terms as chair. His research has focused on the characterization and construction of efficient statistical inference procedures in nonparametric and semiparametric models with an emphasis on regression and time series models, on curve estimation with parametric rates, on inference with incomplete data, and on the empirical likelihood approach. He has published one hundred twenty research papers and guided ten doctoral theses.
Palaniappan Vellaisamy
Is currently a Visiting Professor in the Department of Statistics and Applied Probability, University of California, Santa Barbara, USA. He completed his Ph.D. degree in statistics from the Indian Institute of Technology Kanpur in 1989. He worked as a Research Associate from July 1989 to December 1990 at the Indian Statistical Institute, New Delhi. Then he joined in 1991 as an Assistant Professor in the Department of Mathematics at Indian Institute of Technology Bombay, India. He became a full professor in 2003 and retired in June 2024. His research areas include statistical inference, applied probability, and fractional stochastic processes. He has published more than 120 research papers in various journals of statistics and probability and has guided 11 Ph.D.’s. He is currently an Associate Editor for Statistics and Probability Letters and The Journal of Indian Statistical Association.
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