{"product_id":"9781461448174","title":"Essential Statistical Inference: Theory and Methods","description":"​This book is for students and researchers who have had a first year graduate level mathematicalstatistics 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 numberof examples and problems.\u003cbr\u003eAn important goal has been to make the topics accessible to a wide audience, with little overt relianceon measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimationand testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology.\u003cbr\u003eDennis 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 forresearch concentrated on measurement error, including a co-authored book on non-linear measurementerror models. In recent years the authors have jointly worked on variable selection methods.​\u003cbr\u003e","brand":"Springer New York","offers":[{"title":"Default Title","offer_id":47053687816432,"sku":"9781461448174","price":99.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9781461448174_p0.jpg?v=1763673876","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9781461448174","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}