Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2017

Journal or Book Title

Journal of Statistical Computation and Simulation

Volume

87

Issue

8

First Page

1559

Last Page

1576

DOI

10.1080/00949655.2016.1277426

Abstract

Making predictions of future realized values of random variables based on currently available data is a frequent task in statistical applications. In some applications, the interest is to obtain a two-sided simultaneous prediction interval (SPI) to contain at least k out of m future observations with a certain confidence level based on n previous observations from the same distribution. A closely related problem is to obtain a one-sided upper (or lower) simultaneous prediction bound (SPB) to exceed (or be exceeded) by at least k out of m future observations. In this paper, we provide a general approach for computing SPIs and SPBs based on data from a particular member of the (log)-location-scale family of distributions with complete or right censored data. The proposed simulation-based procedure can provide exact coverage probability for complete and Type II censored data. For Type I censored data, our simulation results show that our procedure provides satisfactory results in small samples. We use three applications to illustrate the proposed simultaneous prediction intervals and bounds.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis as Xie, Yimeng, Yili Hong, Luis A. Escobar, and William Q. Meeker. "A general algorithm for computing simultaneous prediction intervals for the (log)-location-scale family of distributions." Journal of Statistical Computation and Simulation 87, no. 8 (2017): 1559-1576. DOI: 10.1080/00949655.2016.1277426. Posted with permission.

Copyright Owner

Taylor & Francis

Language

en

File Format

application/pdf

Published Version

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