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Copyright (c) 2018 Pakistan Journal of Statistics and Operation Research

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Title

Conditional Inference for the Weibull Extension Model Based on the Generalized Order Statistics

Keywords

Weibull extension model; Modified Weibull model; Weibull distribution; Burr-type XII distribution; Lamox distribution; Generalized Pareto model; Progressive type-II censored samples with binomial random removals; Asymptotic maximum likelihood estimates

Description

In recent years, a new class of models has been proposed to exhibit the bathtub-shaped failure rate functions. The Weibull extension model is one of these models, which is asymptotically related to the ordinary Weibull model and is capable of modeling the bathtub-shaped and increasing failure rate lifetime data. This paper presents the conditional inference for constructing the confidence intervals for the Weibull extension parameters based on the generalized order statistics. For measuring the performances of this approach comparing to the Asymptotic maximum likelihood estimates, Simulation studies have been carried out, that indicated the conditional intervals possess a good statistical properties and they can perform quite well even when the sample size is extremly small. An illustrative examples based on real data are given to illustrate the confidence intervals developed in this paper.


Date

2018-06-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 14 No. 2, 2018



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810