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024 7 $a10.1007/978-1-4614-5369-7
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100   $a20130125d2013      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aSmoothing Spline ANOVA Models /$fby Chong Gu
205   $a2nd ed. 2013.
210  1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2013.
215   $a1 online resource (444 p.)
225 1 $aSpringer Series in Statistics,$x2197-568X ;$v297
300   $aDescription based upon print version of record.
311 08$a1-4899-8984-6 
311 08$a1-4614-5368-2 
320   $aIncludes bibliographical references and indexes.
327   $aIntroduction -- 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.
330   $aNonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data analysis by practitioners. While scores of methods have proved successful for univariate smoothing, ones practical in multivariate settings number far less. Smoothing spline ANOVA models are a versatile family of smoothing methods derived through roughness penalties, that are suitable for both univariate and multivariate problems. In this book, the author presents a treatise on penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with censored life time data and covariates. The unifying themes are the general penalized likelihood method and the construction of multivariate models with built-in ANOVA decompositions. Extensive discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence.
410  0$aSpringer Series in Statistics,$x2197-568X ;$v297
606   $aStatistics
606   $aStatistical Theory and Methods
615  0$aStatistics.
615 14$aStatistical Theory and Methods.
676   $a519.5/38
700   $aGu$b Chong$0474354
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910438148103321
996   $aSmoothing spline ANOVA models$9252362
997   $aUNINA