00907nam0-22003131i-450-99000303284040332120041111124641.0000303284FED01000303284(Aleph)000303284FED0100030328420030910d1969----km-y0itay50------baitaITComunità rurali della Sicilia modernaBronte (1747-1853)Giuseppe Lo Giudice.CataniaFacoltà di Economia dell'Università degli Studi1969.337 P.25 CMAgricolturaSiciliaStoriaE/4H/1.1Lo Giudice,GiuseppeITUNINARICAUNIMARCBK990003032840403321E/4 LOG3586/ISESN3.47110543DECTSSESComunità rurali della Sicilia moderna463469UNINA03622nam 22006135 450 991025545890332120250505001401.0981-10-6557-810.1007/978-981-10-6557-6(CKB)3790000000544588(DE-He213)978-981-10-6557-6(MiAaPQ)EBC5214685(PPN)258852445(PPN)223953903(EXLCZ)99379000000054458820180102d2017 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierStatistical Modelling of Survival Data with Random Effects H-Likelihood Approach /by Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee1st ed. 2017.Singapore :Springer Nature Singapore :Imprint: Springer,2017.1 online resource (XIV, 283 p. 23 illus.) Statistics for Biology and Health,2197-5671981-10-6555-1 Includes bibliographical references and index.Introduction -- Classical Survival Analysis -- H-likelihood Approach to Random-Effects Models -- Simple Frailty Models -- Multi-Component Frailty Models -- Competing Risks Frailty Models -- Variable Selection for Frailty Models -- Mixed-Effects Survival Models -- Joint Model for Repeated Measures and Survival Data -- Further Topics -- A Formula for fitting fixed and random effects -- References -- Index.This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to researchers in medical and genetics fields, graduate students, and PhD (bio) statisticians.      .Statistics for Biology and Health,2197-5671StatisticsBiometryMathematical statisticsData processingStatistical Theory and MethodsBiostatisticsStatistics and ComputingStatistics.Biometry.Mathematical statisticsData processing.Statistical Theory and Methods.Biostatistics.Statistics and Computing.610.72Ha Il Doauthttp://id.loc.gov/vocabulary/relators/aut767638Jeong Jong-Hyeonauthttp://id.loc.gov/vocabulary/relators/autLee Youngjoauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910255458903321Statistical Modelling of Survival Data with Random Effects1961541UNINA