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Loading... Essential Statistical Inference: Theory and Methodsby Dennis D. Boos, Leonard A. Stefanski (Author)
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This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology. Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods. . No library descriptions found. |
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Google Books — Loading... GenresMelvil Decimal System (DDC)519.5Natural sciences and mathematics Mathematics Applied Mathematics, Probabilities Statistical MathematicsLC ClassificationRatingAverage: No ratings.Is this you?Become a LibraryThing Author. |