Nonparametric Bayesian estimation and goodness of fit test
We first make a review of prior distributions neutral to the right, and then we get the Bayes rule for the survival function S(t) = 1 - F(t), with quadratic loss, with these prior distributions. We give, after that, the estimator with a special kind of processes neutral to the right, the homogeneous...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 1985 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3922 |
| Acceso en línea: | https://hdl.handle.net/2099/3922 |
| Access Level: | acceso abierto |
| Palabra clave: | Decision theory Inference Survival Analysis Processes neutral to the right Homogeneous processes Linear approach Teoria de la decisió Inferència Estadística Classificació AMS::62 Statistics::62C Decision theory Classificació AMS::62 Statistics::62G Nonparametric inference Classificació AMS::62 Statistics::62N Survival analysis and censored data |
| Sumario: | We first make a review of prior distributions neutral to the right, and then we get the Bayes rule for the survival function S(t) = 1 - F(t), with quadratic loss, with these prior distributions. We give, after that, the estimator with a special kind of processes neutral to the right, the homogeneous processes. We get in point four the linear Bayes rule and we give there an interpretation of the parameters. We finish with a Bayesian generalization of the Kolmogorov-Smirnov goodness of fit test. |
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