Instrumental Variable Estimation in Binary Regression Measurement Error Models
作者:
L.A. Stefanski,
J.S. Buzas,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 430
页码: 541-550
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476546
出版商: Taylor & Francis Group
关键词: Generalized linear models;Logistic regression;Measurement error;Probit regression
数据来源: Taylor
摘要:
We describe two approaches to instrumental variable estimation in binary regression measurement error models. The methods entail constructing approximate mean models for the binary response as a function of the measured predictor, the instrument, and any covariates in the model. Estimates are obtained by exploiting relationships between regression parameters, just as in linear instrumental variable estimation. In the course of deriving the approximate mean models, we obtain an alternative characterization of instrumental variable estimation in linear measurement error models.
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