In this article, we consider several statistical models for censored exponential data. We prove a large deviation result for the maximum likelihood estimators (MLEs) of each model, and a unique result for the posterior distributions which works well for all the cases. Finally, comparing the large deviation rate functions for MLEs and posterior distributions, we show that a typical feature fails for one model; moreover, we illustrate the relation between this fact and a well-known result for curved exponential models
We prove the large deviation principle (LDP) for posterior distributions arising from subfamilies o...
Abstract: We consider parametric models, not necessarily i.i.d, whose distribu-tions depend on a par...
A new view of the maximum likelihood estimator (MLE) of exponential scale for censored data is prese...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this paper, we consider a class of statistical models with a real-valued threshold parameter, wh...
In this paper, we consider a class of statistical models with a real-valued threshold parameter, wh...
In this paper, we consider a class of statistical models with a real-valued threshold parameter, wh...
In this article, we consider a family of uniform distributions as a statistical model. Assuming that...
We prove the large deviation principle (LDP) for posterior distributions arising from subfamilies o...
We prove the large deviation principle (LDP) for posterior distributions arising from subfamilies o...
In reliability or medical studies, we may only observe each ongoing renewal process for a certain pe...
We prove the large deviation principle (LDP) for posterior distributions arising from subfamilies o...
Abstract: We consider parametric models, not necessarily i.i.d, whose distribu-tions depend on a par...
A new view of the maximum likelihood estimator (MLE) of exponential scale for censored data is prese...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this article, we consider several statistical models for censored exponential data. We prove a la...
In this paper, we consider a class of statistical models with a real-valued threshold parameter, wh...
In this paper, we consider a class of statistical models with a real-valued threshold parameter, wh...
In this paper, we consider a class of statistical models with a real-valued threshold parameter, wh...
In this article, we consider a family of uniform distributions as a statistical model. Assuming that...
We prove the large deviation principle (LDP) for posterior distributions arising from subfamilies o...
We prove the large deviation principle (LDP) for posterior distributions arising from subfamilies o...
In reliability or medical studies, we may only observe each ongoing renewal process for a certain pe...
We prove the large deviation principle (LDP) for posterior distributions arising from subfamilies o...
Abstract: We consider parametric models, not necessarily i.i.d, whose distribu-tions depend on a par...
A new view of the maximum likelihood estimator (MLE) of exponential scale for censored data is prese...