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Record Nr. |
UNINA9910897977403321 |
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Autore |
Florens J. P |
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Titolo |
Nonparametric Bayesian Inference : Contributions by Jean-Marie Rolin / / edited by Jean-Pierre Florens, Michel Mouchart |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (370 pages) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Statistics |
Bayesian Inference |
Bayesian Network |
Estadística bayesiana |
Llibres electrònics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Chapter 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. |
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Sommario/riassunto |
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This 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 |
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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. |
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