Simultaneous best linear invariant prediction of future order statistics for location-scale and scale families and associated optimality properties

TL;DR

This paper develops explicit formulas for optimal linear invariant predictors of future order statistics in location-scale families, demonstrating their efficiency and optimality properties for multiple future observations.

Contribution

It introduces a comprehensive framework for simultaneous prediction of multiple future order statistics with explicit formulas and optimality proofs in location-scale families.

Findings

Explicit formulas for marginal and joint BLIPs of order statistics.

Proven trace and determinant efficiency of the predictors.

Extended results to simultaneous prediction of multiple future order statistics.

Abstract

In this article, we first derive an explicit expression for the marginal best linear invariant predictor (BLIP) of an unobserved future order statistic based on a set of early observed ordered statistics. We then derive the joint BLIPs of two future order statistics and prove that the joint predictors are trace-efficient as well as determinant-efficient linear invariant predictors. More generally, the BLIPs are shown to possess complete mean squared predictive error matrix dominance property in the class of all linear invariant predictors of two future unobserved order statistics. Finally, these results are extended to the case of simultaneous BLIPs of any $\ell$ future order statistics. Both scale and location-scale families of distributions are considered as the parent distribution for the development of results.