Inference of dependent left-truncated and right-censored competing risks data from a general bivariate class of inverse exponentiated distributions

Wang, Liang; Lio, Yuhlong; Tripathi, Yogesh Mani; Dey, Sanku; Zhang, Fode

doi:10.1080/02331888.2022.2047680

Cited by 6 publications

(6 citation statements)

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“…In this paper, we propose a copula-based model for dependent competing risks in the presence of left-truncation. The copula-based competing risks model permits more flexible failure time distributions and dependence structure than the existing competing risks model [4,26,27]. Parametric likelihood-based inference methods are then formulated, motivated by the practically important applications in field reliability analyses.…”

Section: Discussionmentioning

confidence: 99%

“…Survival analysis deals with incomplete failure time data, such as censored and truncated data [1][2][3][4][5]. Left-truncation arises if early failures occur before the data collection period and their failures are simply ignored.…”

Section: Introductionmentioning

confidence: 99%

“…Naïve statistical analyses of truncated, censored, and competing risks data lead to biased inference for the population of interest. Left-truncation, right-censoring, and competing risks are a classical topic in survival analysis that is widely recognized in both biostatistics [3] and reliability engineering [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Existing competing risks analyses for left-truncated field data are limited to the following models: the independent Weibull model with the common shape parameter [26], the Marshall-Olkin bivariate Rayleigh model [27], and the Marshall-Olkin bivariate Weibull model [4]. While these three models provide an important starting point, their models are somewhat specific for modeling latent failure times of competing risks.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we will develop copula-based models for dependent competing risks under left-truncation, permitting more flexible models for dependence structure and failure time distributions than the existing three models of [4,26,27]. Due to our main interest in applications to reliability analyses, we adopt the standard parametric distributions, including the Weibull, lognormal, and gamma distributions.…”

Section: Introductionmentioning

confidence: 99%

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Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

Michimae

Emura

2022

Mathematics

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Survival and reliability analyses deal with incomplete failure time data, such as censored and truncated data. Recently, the classical left-truncation scheme was generalized to analyze “field data”, defined as samples collected within a fixed period. However, existing competing risks models dealing with left-truncated field data are not flexible enough. We propose copula-based competing risks models for latent failure times, permitting a flexible parametric form. We formulate maximum likelihood estimation methods under the Weibull, lognormal, and gamma distributions for the latent failure times. We conduct simulations to check the performance of the proposed methods. We finally give a real data example. We provide the R code to reproduce the simulations and data analysis results.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

Michimae

Emura

2022

Mathematics

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Left‐truncated and right‐censored field failure data: Review of parametric analysis for reliability

Emura

Michimae

2022

Quality & Reliability Eng

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In field reliability analyses, a data collection period is given to monitor the failure events from the field. Left‐truncation arises due to early failures occurring before the data collection period, and right‐censoring arises for late failures occurring beyond the monitoring period. Naïve analyses of left‐truncated and right‐censored data lead to biased estimation of the population lifetime of interest. A variety of models and methods have been developed to analyze the left‐truncated and right‐censored data for field reliability analyses. The goal of the paper is to review the existing models and methods for fitting left‐truncated and right‐censored data. Our review includes the existing statistical models, such as the exponential, Weibull, lognormal, gamma, Gompertz, Lomax, and spline models. We comprehensively review the statistical issues of maximum likelihood estimation, model selection, residual lifetime prediction, and Bayesian methods. Some of these methods are illustrated through the field reliability analysis of the electric power transformer dataset.

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Analyzing Burr-X competing risk model using adaptive progressive Type-II censored binomial removal data with application to electrodes and electronics

Alqasem,

Nassar,

Abd Elwahab

et al. 2024

Journal of Radiation Research and Applied Sciences

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Inference of dependent left-truncated and right-censored competing risks data from a general bivariate class of inverse exponentiated distributions

Cited by 6 publications

References 38 publications

Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies

Left‐truncated and right‐censored field failure data: Review of parametric analysis for reliability

Analyzing Burr-X competing risk model using adaptive progressive Type-II censored binomial removal data with application to electrodes and electronics

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