Smoothing Spline Anova Models - (Springer Statistics) 2nd Edition by Chong Gu (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br><p>Updated to include the latest computational methods, this second edition explains how to use the 'gss' R package and features expanded empirical studies, a reorganized content, and a further new appendix analyzing new and controversial topics in smoothing.</p><p/><br></br><p><b> Book Synopsis </b></p></br></br>Introduction.- Model Construction.- Regression with Gaussian-Type Responses.- More Splines.- Regression and Exponential Families.- Regression with Correlated Responses.- Probability Density Estimation.- Hazard Rate Estimation.- Asymptotic Convergence.- Penalized Pseudo Likelihood.<p/><br></br><p><b> From the Back Cover </b></p></br></br><p>Nonparametric function estimation with stochastic data, otherwise</p><p>known as smoothing, has been studied by several generations of</p><p>statisticians. Assisted by the ample computing power in today's</p><p>servers, desktops, and laptops, smoothing methods have been finding</p><p>their ways into everyday data analysis by practitioners. While scores</p><p>of methods have proved successful for univariate smoothing, ones</p><p>practical in multivariate settings number far less. Smoothing spline</p><p>ANOVA models are a versatile family of smoothing methods derived</p><p>through roughness penalties, that are suitable for both univariate and</p><p>multivariate problems.</p><p>In this book, the author presents a treatise on penalty smoothing</p><p>under a unified framework. Methods are developed for (i) regression</p><p>with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a</p><p>variety of sampling schemes; and (iii) hazard rate estimation with</p><p>censored life time data and covariates. The unifying themes are the</p><p>general penalized likelihood method and the construction of</p><p>multivariate models with built-in ANOVA decompositions. Extensive</p><p>discussions are devoted to model construction, smoothing parameter</p><p>selection, computation, and asymptotic convergence.</p><p/><br></br><p><b> Review Quotes </b></p></br></br><br><p>"The purpose of the book is to comprehensively present smoothing and penalized splines from the point of view of reproducing kernel Hilbert spaces (RKHS). ... the book makes a valuable contribution to the literature on smoothing and penalized splines, especially for more mathematically oriented researchers." (W. John Braun, Technometrics, Vol. 56 (4), November, 2014)</p><br><p/><br></br><p><b> About the Author </b></p></br></br><p>Chong Gu received his Ph.D. from University of Wisconsin-Madison in 1989, and has been on the faculty in Department of Statistics, Purdue University since 1990. At various times during his career, he has held visiting appointments at University of British Columbia, University of Michigan, and National Institute of Statistical Sciences.</p>
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