多状态模型的生活史分析:综述及一些问题

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Canadian Journal of Statistics-Revue Canadienne De Statistique Pub Date : 2022-07-06 DOI:10.1002/cjs.11711
Richard J. Cook, Jerald F. Lawless
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引用次数: 0

摘要

生命史分析在过去50年中发展成为一种分析与人类健康、教育、就业和其他领域相关过程的方法。许多过程的复杂性,难以获得完整准确的数据,以及越来越多地使用登记处和行政来源的观测数据,这些都构成了最近的许多挑战。我们回顾了生命史分析的演变,讨论了最近的一些工作,并考虑了目前备受关注的三个领域。我们强调的一个主题是使用扩展模型,除了感兴趣的生活史过程外,还包括研究的选择和观察过程。介绍了健康研究的例子。
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Life history analysis with multistate models: A review and some current issues

Life history analysis has evolved in the last 50 years as a methodology for analyzing processes associated with human health, education, employment, and other areas. The complexity of many processes, the difficulty of obtaining complete and accurate data, and the increased use of observational data from registries and administrative sources have posed many recent challenges. We review the evolution of life history analysis, discuss some recent work, and consider three areas currently receiving much attention. A theme we stress is the use of expanded models that include selection and observation processes for studies in addition to the life history process of interest. Examples from health research are presented.

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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
62
审稿时长
>12 weeks
期刊介绍: The Canadian Journal of Statistics is the official journal of the Statistical Society of Canada. It has a reputation internationally as an excellent journal. The editorial board is comprised of statistical scientists with applied, computational, methodological, theoretical and probabilistic interests. Their role is to ensure that the journal continues to provide an international forum for the discipline of Statistics. The journal seeks papers making broad points of interest to many readers, whereas papers making important points of more specific interest are better placed in more specialized journals. The levels of innovation and impact are key in the evaluation of submitted manuscripts.
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Issue Information Issue Information Issue Information Censored autoregressive regression models with Student-t innovations Acknowledgement of referees' services remerciements aux membres des jurys
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