{"title":"A copula duration model with dependent states and spells","authors":"Simon M.S. Lo , Shuolin Shi , Ralf A. Wilke","doi":"10.1016/j.csda.2024.108104","DOIUrl":null,"url":null,"abstract":"<div><div>A nested Archimedean copula model for dependent states and spells is introduced and the link to a classical survival model with frailties is established. The model relaxes an important restriction of classical survival models as the distributions of unobservable heterogeneities are permitted to depend on the observable covariates. Its modular structure has practical advantages as the different components can be separately specified and estimation can be done sequentially or separately. This makes the model versatile and adaptable in empirical work. An application to labour market transitions with linked administrative data supports the need for a flexible specification of the dependence structure and the model for the marginal survivals. The conventional Markov Chain Model is shown to give sizeably biased results in the application.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"204 ","pages":"Article 108104"},"PeriodicalIF":1.6000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Statistics & Data Analysis","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167947324001889","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/28 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
A nested Archimedean copula model for dependent states and spells is introduced and the link to a classical survival model with frailties is established. The model relaxes an important restriction of classical survival models as the distributions of unobservable heterogeneities are permitted to depend on the observable covariates. Its modular structure has practical advantages as the different components can be separately specified and estimation can be done sequentially or separately. This makes the model versatile and adaptable in empirical work. An application to labour market transitions with linked administrative data supports the need for a flexible specification of the dependence structure and the model for the marginal survivals. The conventional Markov Chain Model is shown to give sizeably biased results in the application.
期刊介绍：
Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas:
I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article.
II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures.
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III) Special Applications - [...]
IV) Annals of Statistical Data Science [...]
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