Simulation-based Inference in a Zero-inflated Bernoulli Regression Model
Simulation-based Inference in a Zero-inflated Bernoulli Regression Model
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DOI：
10.1080/03610918.2014.950743
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发表时间：
2016-01-01
影响因子：
0.9
通讯作者：
Dupuy, Jean-Francois
中科院分区：
文献类型：
--
作者：
Diop, Aba;Diop, Aliou;Dupuy, Jean-Francois
逻辑回归模型已成为研究二元结果与一组潜在预测因子之间关系的标准工具。在分析二进制数据时，经常会出现观察到的零比例大于假设的逻辑模型下的预期。零膨胀二项（ZIB）模型已被开发用于拟合包含太多零的二进制数据。在这些模型中的极大似然估计已被提出，并建立其渐近性质。然而，ZIB模型的几个方面仍然值得关注，例如赔率和事件概率的估计。在这篇文章中，我们提出了这些数量的估计，我们调查他们的性质，理论上和通过模拟。基于这些结果，我们提供了建议的范围内的条件（最小样本量，最大比例的零超过）下，一个可靠的统计推断的比值比和事件概率可以在ZIB回归模型。一个真实数据的例子说明了所提出的估计。
The logistic regression model has become a standard tool to investigate the relationship between a binary outcome and a set of potential predictors. When analyzing binary data, it often arises that the observed proportion of zeros is greater than expected under the postulated logistic model. Zero-inflated binomial (ZIB) models have been developed to fit binary data that contain too many zeros. Maximum likelihood estimators in these models have been proposed and their asymptotic properties established. Several aspects of ZIB models still deserve attention however, such as the estimation of odds-ratios and event probabilities. In this article, we propose estimators of these quantities and we investigate their properties both theoretically and via simulations. Based on these results, we provide recommendations about the range of conditions (minimum sample size, maximum proportion of zeros in excess) under which a reliable statistical inference on the odds-ratios and event probabilities can be obtained in a ZIB regression model. A real-data example illustrates the proposed estimators.