03363nam 22006135 450 991043814810332120250609112105.01-299-33751-11-4614-5369-010.1007/978-1-4614-5369-7(CKB)2550000001017943(EBL)1081906(OCoLC)827212496(SSID)ssj0000870777(PQKBManifestationID)11523247(PQKBTitleCode)TC0000870777(PQKBWorkID)10819329(PQKB)11620227(DE-He213)978-1-4614-5369-7(MiAaPQ)EBC1081906(PPN)168302853(MiAaPQ)EBC4067963(EXLCZ)99255000000101794320130125d2013 u| 0engur|n|---|||||txtccrSmoothing Spline ANOVA Models /by Chong Gu2nd ed. 2013.New York, NY :Springer New York :Imprint: Springer,2013.1 online resource (444 p.)Springer Series in Statistics,2197-568X ;297Description based upon print version of record.1-4899-8984-6 1-4614-5368-2 Includes bibliographical references and indexes.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.Nonparametric 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.Springer Series in Statistics,2197-568X ;297StatisticsStatistical Theory and MethodsStatistics.Statistical Theory and Methods.519.5/38Gu Chong474354MiAaPQMiAaPQMiAaPQBOOK9910438148103321Smoothing spline ANOVA models252362UNINA