LEADER 03262nam 22005415 450 001 9910897977403321 005 20260121123002.0 010 $a3-031-61329-5 024 7 $a10.1007/978-3-031-61329-6 035 $a(CKB)36382215300041 035 $a(MiAaPQ)EBC31735070 035 $a(Au-PeEL)EBL31735070 035 $a(DE-He213)978-3-031-61329-6 035 $a(EXLCZ)9936382215300041 100 $a20241022d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNonparametric Bayesian Inference $eContributions by Jean-Marie Rolin /$fedited by Jean-Pierre Florens, Michel Mouchart 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (370 pages) 311 08$a3-031-61328-7 327 $aChapter 1. On the a-Algebraic Realization Problem -- Chapter 2. Weak Conditional Independence And Relative Invariance in Bayesian Statistics -- Chapter 3. Some Useful Properties of the Dirichlet Process -- Chapter 4. On the Distribution of Jumps of the Dirichlet Process -- Chapter 5. Bayes, Bootstrap, Moments -- Chapter 6. Smooth vs. likelihood estimation for a class of mixtures of discrete distributions -- Chapter 7. Bayesian Encompassing Specification Tests of a Parametric Model against a Non Parametric Alternative -- Chapter 8. Nonparametric Bayesian Survival Analysis -- Chapter 9. Simulation of Posterior Distributions in Nonparametric Censored Analysis -- Chapter 10. Bayesian Identification of Semi-Parametric Binary Response Models -- Chapter 11. Survival Data with Explanatory Processes: A Full Nonparametric Bayesian Analysis -- Chapter 12. Nonparametric Competing Risks Models: Identification and Strong Consistency. 330 $aThis book is a compilation of unpublished papers written by Jean-Marie Rolin (with several co-authors) on nonparametric bayesian estimation. Jean-Marie was professor of statistics at University of Louvain and died on November 5th, 2018. He made important contributions in mathematical statistics with applications to different fields like econometrics or biometrics.These papers cover a variety of topics, including: ? The Mathematical structure of the Bayesian model and the main concepts (sufficiency, ancillarity, invariance?) ? Representation of the Dirichlet processes and of the associated Polya urn model and applications to nonparametric bayesian analysis. ? Contributions to duration models and to their non parametric bayesian treatment. 606 $aStatistics 606 $aStatistics 606 $aBayesian Inference 606 $aBayesian Network 606 $aEstadística bayesiana$2thub 608 $aLlibres electrònics$2thub 615 0$aStatistics. 615 14$aStatistics. 615 24$aBayesian Inference. 615 24$aBayesian Network. 615 7$aEstadística bayesiana 676 $a519.5 700 $aFlorens$b J. P$055643 701 $aMouchart$b Michel$055644 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910897977403321 996 $aNonparametric Bayesian Inference$94211480 997 $aUNINA