The regression–calibration method for fitting generalized linear models with additive measurement error
James W. Hardin
Arnold School of Public Health
University of South Carolina
Columbia, SC 29208
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Henrik Schmiediche
Department of Statistics MS-3143
Texas A&M University
College Station, TX 77843-3143
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Raymond J. Carroll
Department of Statistics MS-3143
Texas A&M University
College Station, TX 77843-3143
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Abstract. This paper discusses and illustrates the method of regression calibration.
This is a straightforward technique for fitting models with additive
measurement error. We present this discussion in terms of generalized linear
models (GLMs) following the notation defined in Hardin and Carroll (2003).
Discussion will include specified measurement error, measurement error
estimated by replicate error-prone proxies, and measurement error estimated
by instrumental variables. The discussion focuses on software developed as
part of a small business innovation research (SBIR) grant from the National
Institutes of Health (NIH).
View all articles by these authors:
James W. Hardin, Henrik Schmiediche, Raymond J. Carroll
View all articles with these keywords:
regression calibration, measurement error, instrumental variables, replicate measures, generalized linear models
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