William Volterman

Simultaneous Pitman closeness of progressively type-II right-censored order statistics to population quantiles

In this work, we extend prior results concerning the simultaneous Pitman closeness of order statistics (OS) to population quantiles. By considering progressively type-II right-censored samples, we derive expressions for the simultaneous closeness probabilities of the progressively censored OS to population quantiles. Explicit expressions are deduced for the cases when the underlying distribution has bounded and unbounded supports. Illustrations are provided for the cases of exponential, uniform and normal distributions for various progressive type-II right-censoring schemes and different quantiles. Finally, an extension to the case of generalized OS is outlined.

Exact nonparametric meta-analysis for multiple independent doubly Type-II censored samples

Nonparametric inferential methods are discussed for the situation of multiple independent doubly Type-II censored samples. The basic distribution theory for the pooled sample is given assuming that the underlying distribution is continuous and it is demonstrated how the weights in the mixture representations of the pooled order statistics as a mixture of the usual order statistics can be obtained. This is used to construct nonparametric prediction intervals, tolerance intervals for a future sample, and confidence intervals for a population quantile. A small simulation study compares the exact coverage probabilities for population quantiles, to those obtained where the mixture weights are generated by simulation.

Two-sample Pitman closeness comparison under progressive Type-II censoring

In this paper, we consider two problems concerning two independent progressively Type-II censored samples. We first consider the Pitman closeness (PC) of order statistics from two independent progressively censored samples to a specific population quantile. We then consider the point prediction of a future progressively censored order statistic and discuss the determination of the closest progressively censored order statistic from the current sample according to the simultaneous closeness probabilities. For both these problems, explicit expressions are derived for the pertinent PC probabilities, and then special cases are given as examples. For various censoring schemes, we also present numerical results for the standard uniform, standard exponential, and standard normal distributions. Finally, a distribution-free result for the median is obtained.

Nonparametric prediction of future order statistics

Prediction of censored order statistics from a Type-II censored sample can be done with trivial bounds having perfect confidence. However, given independent samples from the same absolutely continuous distribution, improved bounds can be attained. In this regard, we develop here point prediction based on L-statistics for predicting order statistics (OS) from a future sample as well as for predicting censored OS from a Type-II censored sample. An example is taken to illustrate these ideas, and the limiting case wherein a single independent sample is arbitrarily large is also discussed.

A Meta-Analysis of Multisample Type-II Censored Data With Parametric and Nonparametric Results

We discuss meta-analysis of multiple s-independent Type-II right censored data. In particular, we consider parametric inference using Best Linear Unbiased Estimation, as well as non-parametric inference. We provide pertinent numerical results and two examples to illustrate all the methods of inference developed here.

Further Results on Order Statistics Generated by Two Simulation Methods

Uniform order statistics generated by two simulation methods are compared by means of Pitman’s measure of closeness. This measure, as a probability, is shown to be asymptotically 1/2. Some results are also established for fixed points of the cumulative distribution function (CDF) for a uniform order statistic. These fixed points are important for calculations involving the joint distribution of these order statistics.