{"title":"A latent variable approach for modeling recall-based time-to-event data with Weibull distribution","authors":"","doi":"10.1007/s00180-023-01444-3","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>The ability of individuals to recall events is influenced by the time interval between the monitoring time and the occurrence of the event. In this article, we introduce a non-recall probability function that incorporates this information into our modeling framework. We model the time-to-event using the Weibull distribution and adopt a latent variable approach to handle situations where recall is not possible. In the classical framework, we obtain point estimators using expectation-maximization algorithm and construct the observed Fisher information matrix using missing information principle. Within the Bayesian paradigm, we derive point estimators under suitable choice of priors and calculate highest posterior density intervals using Markov Chain Monte Carlo samples. To assess the performance of the proposed estimators, we conduct an extensive simulation study. Additionally, we utilize age at menarche and breastfeeding datasets as examples to illustrate the effectiveness of the proposed methodology.</p>","PeriodicalId":55223,"journal":{"name":"Computational Statistics","volume":"23 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s00180-023-01444-3","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
The ability of individuals to recall events is influenced by the time interval between the monitoring time and the occurrence of the event. In this article, we introduce a non-recall probability function that incorporates this information into our modeling framework. We model the time-to-event using the Weibull distribution and adopt a latent variable approach to handle situations where recall is not possible. In the classical framework, we obtain point estimators using expectation-maximization algorithm and construct the observed Fisher information matrix using missing information principle. Within the Bayesian paradigm, we derive point estimators under suitable choice of priors and calculate highest posterior density intervals using Markov Chain Monte Carlo samples. To assess the performance of the proposed estimators, we conduct an extensive simulation study. Additionally, we utilize age at menarche and breastfeeding datasets as examples to illustrate the effectiveness of the proposed methodology.
期刊介绍：
Computational Statistics (CompStat) is an international journal which promotes the publication of applications and methodological research in the field of Computational Statistics. The focus of papers in CompStat is on the contribution to and influence of computing on statistics and vice versa. The journal provides a forum for computer scientists, mathematicians, and statisticians in a variety of fields of statistics such as biometrics, econometrics, data analysis, graphics, simulation, algorithms, knowledge based systems, and Bayesian computing. CompStat publishes hardware, software plus package reports.
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