Two-part regression models for longitudinal zero-inflated count data
Two-part regression models for longitudinal zero-inflated count data
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DOI：
10.1002/cjs.10056
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发表时间：
2010-06-01
影响因子：
0.6
通讯作者：
Maruotti, Antonello
中科院分区：
文献类型：
--
作者：
Alfo, Marco;Maruotti, Antonello
两部分模型在经济学文献中建立得相当好，因为它们准确地类似于委托代理类型的模型，其中同质的，可观察的，可计数的结果受到（先验的，外生的）选择的影响。第一个决策可以用一个二元选择模型来表示，使用概率单位或对数单位链接来建模;第二个决策可以通过截断离散分布来分析，如截断泊松分布、负二项分布等，直到最近，人们才特别注意将两部分模型扩展到处理纵向数据。作者讨论了动态两部分模型的半参数估计方法，并提出了与其他，完善的替代品的比较。影响第一级决策过程的异质性来源，即使用某种服务的决定，被认为也会影响积极结果的（截断）分布。估计是通过EM算法进行的，没有对随机效应分布的参数假设。此外，作者研究了有限混合表示的扩展，以允许这些部分中的每个部分中的组件之间的不可观察的过渡。所提出的模型进行了讨论，使用经验以及模拟数据。加拿大统计杂志38：197-216; 2010（C）2010年加拿大统计学会
Two-part models are quite well established in the economic literature, since they resemble accurately a principal-agent type model, where homogeneous, observable, counted outcomes are subject to a (prior, exogenous) selection choice. The first decision can be represented by a binary choice model, modeled using a probit or a logit link; the second can be analyzed through a truncated discrete distribution such as a truncated Poisson, negative binomial, and so on. Only recently, a particular attention has been devoted to the extension of two-part models to handle longitudinal data. The authors discuss a semi-parametric estimation method for dynamic two-part models and propose a comparison with other, well-established alternatives. Heterogeneity sources that influence the first level decision process, that is, the decision to use a certain service, are assumed to influence also the (truncated) distribution of the positive outcomes. Estimation is carried out through an EM algorithm without parametric assumptions on the random effects distribution. Furthermore, the authors investigate the extension of the finite mixture representation to allow for unobservable transition between components in each of these parts. The proposed models are discussed using empirical as well as simulated data. The Canadian Journal of Statistics 38: 197-216; 2010 (C) 2010 Statistical Society of Canada