Bayesian life test planning for the Weibull distribution with given shape parameter

Bayesian Life Test Planning for the Weibull Distribution with Given Shape Parameter

This paper describes Bayesian methods for life test planning with Type II censored data from a Weibull distribution, when the Weibull shape parameter is given. We use conjugate prior distributions and criteria based on estimating a quantile of interest of the lifetime distribution. One criterion is based on a precision factor for a credibility interval for a distribution quantile and the other ...

Limited failure-censored life test for the Weibull distribution

When the distribution of lifetimes is 2-parameter exponential, Balasooriya [6] provided a failure-censored reliability sampling-plan to save test time. This paper extends the Balasooriya sampling plan to the Weibull distribution and provides a limited failure-censored reliability sampling plan (LFCR) to do life testing when test facilities are scarce. The -expected test time of the LFCR is comp...

Bayesian Designing and Analysis of Simple Step-Stress Accelerated Life Test with Weibull Lifetime Distribution

the test for adverse selection in life insurance market: the case of mellat insurance company

انتخاب نامساعد یکی از مشکلات اساسی در صنعت بیمه است. که ابتدا در سال 1960، توسط روتشیلد واستیگلیتز مورد بحث ومطالعه قرار گرفت ازآن موقع تاکنون بسیاری از پژوهشگران مدل های مختلفی را برای تجزیه و تحلیل تقاضا برای صنعت بیمه عمر که تماما ناشی از عدم قطعیت در این صنعت میباشد انجام داده اند .وهدف از آن پیدا کردن شرایطی است که تحت آن شرایط انتخاب یا کنار گذاشتن یک بیمه گزار به نفع و یا زیان شرکت بیمه ...

Bayesian Estimation of Shift Point in Shape Parameter of Inverse Gaussian Distribution Under Different Loss Functions

In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using variou...