Large-Sample Approximations to the Best Linear Unbiased Estimation and Best Linear Unbiased Prediction Based on Progressively Censored Samples and Some Applications

In this paper, we consider the situation where a life-testing experiment yields a Type-II progressively censored sample. We then develop large-sample approximations to the best linear unbiased estimators for the scale-parameter as well as for the location-scale parameter families of distributions. Large-sample expressions are also derived for the variances and covariance of these estimators. These results are used further to develop large-sample approximations to the best linear unbiased predictors of future failures. Finally, we present two examples in order to illustrate the methods of inference developed in this paper.