Testing exponentiality based on Progressively Type-I interval censored data

In this paper we propose non-parametric estimates for the information measure entropy when a progressively Type-I interval censored data is available. Different non-parametric approaches are used for deriving the estimates. Entropy-based tests of exponentiality are proposed. The critical values and the power values of the proposed tests are simulated and studied under various alternatives. Real life data sets are presented and analysed.

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Publication Details

Journal
Statistics Optimization & Information Computing
Published
2025-08-13
DOI
https://doi.org/10.19139/soic-2310-5070-2394
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
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article

Testing exponentiality based on Progressively Type-I interval censored data

Huda H. Qubbaj
Statistics Optimization & Information Computing
Statistical Distribution Estimation and Applications
article

Testing exponentiality based on Progressively Type-I interval censored data

Huda H. Qubbaj
article en

Abstract

In this paper we propose non-parametric estimates for the information measure entropy when a progressively Type-I interval censored data is available. Different non-parametric approaches are used for deriving the estimates. Entropy-based tests of exponentiality are proposed. The critical values and the power values of the proposed tests are simulated and studied under various alternatives. Real life data sets are presented and analysed.

Statistics Optimization & Information ComputingVol. 14(5)
University of Jordan (JO)
Openalex Percentile: Top 71%
Statistical Distribution Estimation and Applications
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Testing exponentiality based on Progressively Type-I interval censored data — Huda H. Qubbaj · Statistics Optimization & Information Computing (2025) | TGRS Research Map | TGRS