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"This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students"--
This revised book presents theoretical results relevant to Edgeworth and saddlepoint expansions to densities and distribution functions. Variants on these expansions, including much of modern likelihood theory, are discussed and applications to lattice distributions are extensively treated.
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