By Dennis D. Boos,Leonard A . Stefanski
This ebook is for college students and researchers who've had a primary yr graduate point mathematical statistics path. It covers classical probability, Bayesian, and permutation inference; an advent to uncomplicated asymptotic distribution thought; and sleek themes like M-estimation, the jackknife, and the bootstrap. R code is woven in the course of the textual content, and there are a wide number of examples and problems.
An very important target has been to make the themes available to a large viewers, with little overt reliance on degree conception. A general semester direction comprises Chapters 1-6 (likelihood-based estimation and trying out, Bayesian inference, easy asymptotic effects) plus decisions from M-estimation and similar trying out and resampling methodology.
Dennis Boos and Len Stefanski are professors within the division of records at North Carolina State. Their examine has been eclectic, frequently with a robustness perspective, even though Stefanski is additionally identified for research focused on dimension blunders, together with a co-authored e-book on non-linear measurement error types. in recent times the authors have together labored on variable choice methods.
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Extra resources for Essential Statistical Inference: Theory and Methods: 120 (Springer Texts in Statistics)
Essential Statistical Inference: Theory and Methods: 120 (Springer Texts in Statistics) by Dennis D. Boos,Leonard A . Stefanski