http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Structural logistic regression measurement error models
We study structural measurement error models when the reponse Y and true predictor X follow a logistic regression model and the observed proxy W satisfies a conditional independence assumption. Assuming that a structural measurement error model is appropriate, we compare two approaches to estimation. In the first approach no assumptions about the distribution of X are made; in the second approach it is assumed that X has a normal distribution. When the normality assumption holds, the second approach yields a consistent and asymptotically efficient estimator; however, consistency is lost when the normality assumption is violated. The first approach yields consistent estimators irrespective of the distribution of X; however, it sacrifices efficiency when X is normally distributed. In the latter case we examine the loss of efficiency for some parametric specifications of known importance.
Offprint: Proceedings of the Arcata Conference on Errors-in-variable.
Includes bibliographical references (leaves 8-9).
Supported by the National Institutes of Health
College Station, Texas : Department of Statistics, Texas A & M University, [1990]
Technical report (Texas A & M University. Department of Statistics) ; no. 73