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

Achim Dörre, Chung Yan Huang, Yi Kuan Tseng, Takeshi Emura

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish
Pages (from-to)375-408
Number of pages34
JournalComputational Statistics
Volume36
Issue number1
DOIs
StatePublished - Mar 2021

Keywords

  • Accelerated life testing
  • Confidence band
  • Confidence interval
  • Newton–Raphson algorithm
  • Reliability
  • Weibull distribution

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