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Essential Statistical Inference Boos, Dennis D. Springer
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Sinopse

?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?


Detalhes do produto

Peso: 8,8 kg
Número de páginas: 588
Ano de edição: 2012
ISBN 10: 1489987932
ISBN 13: 9781489987938
Altura: 3
Largura: 16
Comprimento: 24
Idioma : Inglês
Tipo de produto : Livro
Assuntos : Estatística
Assuntos : Universitário Técnico


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