Likelihood-based analysis of doubly-truncated data under the location-scale and AFT model

Achim Dörre, Chung-Yan Huang, Yi-Kuan Tseng and Takeshi Emura ()
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Achim Dörre: University of Rostock
Chung-Yan Huang: National Central University
Yi-Kuan Tseng: National Central University
Takeshi Emura: Chang Gung University

Computational Statistics, 2021, vol. 36, issue 1, No 16, 375-408

Abstract: Abstract Doubly-truncated data arise in many fields, including economics, engineering, medicine, and astronomy. This article develops likelihood-based inference methods for lifetime distributions under the log-location-scale model and the accelerated failure time model based on doubly-truncated data. These parametric models are practically useful, but the methodologies to fit these models to doubly-truncated data are missing. We develop algorithms for obtaining the maximum likelihood estimator under both models, and propose several types of interval estimation methods. Furthermore, we show that the confidence band for the cumulative distribution function has closed-form expressions. We conduct simulations to examine the accuracy of the proposed methods. We illustrate our proposed methods by real data from a field reliability study, called the Equipment-S data.

Keywords: Reliability; Confidence band; Confidence interval; Accelerated life testing; Newton–Raphson algorithm; Weibull distribution (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:36:y:2021:i:1:d:10.1007_s00180-020-01027-6

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DOI: 10.1007/s00180-020-01027-6

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