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A Study on Non-ignorable Nonresponse Problems in Generalized Linear Models

广义线性模型中不可忽略的无响应问题的研究

基本信息

批准号：
12680310
负责人：
KANAZAWA Yuichiro
金额：
$ 1.34万
依托单位：
University of Tsukuba
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2003
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12680310/
关键词：
EM Algorithm Non-ignorable nonresponse Missing completely at random Missing at random Multiple imputation Proportional-odds model Logistic regression model Structural equation model Structural equation model False negatives

项目摘要

Generalized linear models (GLM) with non-ignorably missing response or covariates has been studied since 1996 by Baker, Chen, Clifford, Fitzmaurice, Heath, Ibrahim, Laird, Lipshitz, Zahner. Their proposed method of estimation does not consider inestimability of the model parameters and their standard errors. In the fifth paper listed below, we uncovered the pattern of inestimability when some of the covariates in GLM are non-ignorably missing. In the fourth paper listed below, we again uncovered the pattern of inestimability when some of the resposes in GLM are non-ignorably missing. In this paper we also theoretically showed that if some of the responses are ignorably missing, then analysis of the listwisely deleted data will yield identical results with those using the method proposed by Ibrahim et al. in 1996 Biometrics paper. We also extended their parameter estimations method when there are non-ignorable non-responses in dependent variable to proportional-odds model and apply the … More method to total cast of ownership estimation in automobile markets in the U.S. in the paper listed third belowStructural equation models are widely recognized as an important tool for social sciences. The recent progresses in statistical software specializing in structural equation modeling such as LISREL, EQS, and AMOS accelerated its usage. In cases where each measurements are categorical, the model requires much more sophisticated approach and there does not seem to be an established consensus in how those categorical data ought to be analyzed. The recent advances in treating missing values in structural equation models are reviewed in Allison (2003) in which the limitations are pointed out of the more traditional methods such as listwise deletion or pairwise deletion and the more modern maximum likelihood estimation methods are explored. Also the underlying ideas and the actual implementation of multiple imputation methods are broadly discussed. This year we thoroughly reviewed the preceding progresses in this field and explore its problems in estimation using the real-world data in the first two papers listed below Less

自1996年以来，Baker，Chen，Clifford，Fitzmaurice，Heath，Ibrahim，Laird，Lipshitz，Zahner研究了具有不可重复缺失响应或协变量的广义线性模型（GLM）。他们提出的估计方法不考虑模型参数及其标准误差的不可估计性。在下面列出的第五篇论文中，我们揭示了当GLM中的一些协变量不可估计地缺失时的不可估计性模式。在下面列出的第四篇论文中，我们再次揭示了当GLM中的一些响应不可估计地缺失时的不可估计性模式。在本文中，我们还从理论上表明，如果一些响应是可识别的缺失，然后分析列表删除的数据将产生相同的结果与使用的方法提出的Ibrahim等人。在1996年生物统计学论文。我们还将他们在因变量中存在不可消除的无响应时的参数估计方法推广到比例优势模型，并应用 ...更多信息结构方程模型被广泛认为是社会科学研究的重要工具。近年来，LISREL、EQS、AMOS等结构方程建模统计软件的发展，加速了结构方程建模的应用。在每个测量都是分类的情况下，模型需要更复杂的方法，并且在如何分析这些分类数据方面似乎没有达成共识。Allison（2003）综述了结构方程模型中缺失值处理的最新进展，指出了列表删除和成对删除等传统方法的局限性，并探讨了现代最大似然估计方法。此外，基本的思想和多重插补方法的实际执行进行了广泛的讨论。今年，我们全面回顾了这一领域的先前进展，并在下面列出的前两篇论文中探讨了使用真实世界数据进行估计的问题。