Exhaustivité, ancillarité et identification en statistique bayesienne
A Bayesian experiment is defined by a unique probability on the product of the parameter space and the sample space. This joint probability determines a conditional independance relation which is used for a symmetrical analysis of sufficiency and ancillarity on the parameter and the sample. Identification is then considered as a property of minimal sufficiency on the parameter space. These concepts are extended to conditional models and are shown to be suitable for a study of the exogeneity property in a coherent statistical framework.
Year of publication: |
1986
|
---|---|
Authors: | FLORENS, Jean-Pierre ; MOUCHART, Michel |
Published in: |
Annales d'Economie et de Statistique. - École Nationale de la Statistique et de l'Admnistration Économique (ENSAE). - 1986, 4, p. 63-93
|
Publisher: |
École Nationale de la Statistique et de l'Admnistration Économique (ENSAE) |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Duration models and point processes
Florens, Jean-Pierre, (2007)
-
Weak conditional independence and relative invariance in Bayesian statistics
Florens, Jean-Pierre, (1990)
-
Invariance arguments in Bayesian statistics
Florens, Jean-Pierre, (1989)
- More ...