Proportional Hazards models have been widely used to analyze survival data. In many cases survival data do not verify the assumption of proportional hazards. An alternative to the PH models with more relaxed conditions are Accelerated Failure Time models. These models are fairly commonly used in the field of manufacturing, but they are more and more frequent for modeling clinical trial data. Th...

In this article, we consider the doubly type-1 censoring scheme that researchers frequently use in clinical trials and lifetime experiments. The Bayesian paradigm will be used to estimate parameters of Geometric Lifetime Model (GLTM) using a type-I scheme. Bayes estimators their associated risks are examined terms closed-form algebraic expressions. This research also includes strategy for elici...

The progressive censoring scheme is a method of data collecting in reliability and life testing which has been of intensified interest in recent years. In the present paper, we prove some characterization results on generalized Pareto distribution based upon the independency and expected values of some functions of progressive type-II right censored order statistics.

This paper, determines the confidence interval using the Fisher information under progressive type-II censoring for the k-step exponential step-stress accelerated life testing. We study the performance of these confidence intervals. Finally an example is given to illustrate the proposed procedures.

Let X be a random vector taking values in IR d , let Y be a bounded random variable, and let C be a right censoring random variable operating on Y. It is assumed that C is independent of (X; Y), the distribution function of C is continuous and the support of the distribution of Y is a proper subset of the support of the distribution of C. Given a sample fX i ; minfY i ; C i g; I Y i C i ] g and...

While the commonly used log-rank test for survival times between 2 groups enjoys many desirable properties, sometimes the log-rank test and its related linear rank tests perform poorly when sample sizes are small. Similar concerns apply to interval estimates for treatment differences in this setting, though their properties are less well known. Standard permutation tests are one option, but the...

Bayesian predictive functions for future observations from a generalized Pareto distribution based on generalized order statistics are obtained. Two cases are considered unknown one parameter and unknown two parameters. We also consider two cases fixed sample size and random sample size. The Bayesian predictive functions are specialized to ordinary order statistics, progressive type II censorin...

Bayesian predictive functions for future observations from a generalized Pareto distribution based on generalized order statistics are obtained. Two cases are considered unknown one parameter and unknown two parameters. We also consider two cases fixed sample size and random sample size. The Bayesian predictive functions are specialized to ordinary order statistics, progressive type II censorin...