Score tests in a generalized linear model with surrogate covariates

J. H. Sepanski

doi:10.1016/0167-7152(92)90277-C

Score tests in a generalized linear model with surrogate covariates

J. H. Sepanski

Statistics, Actuarial & Data Sciences

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

We consider generalized linear models where a predictor is measured with error. The efficient score test for the effect of that predictor depends on the regression of the true predictor on its observed surrogate. Using validation data, we estimate the regression by nonparametric techniques. The resulting semiparametric score test is shown to be nearly asymptotically efficient.

Original language	English
Pages (from-to)	1-10
Number of pages	10
Journal	Statistics and Probability Letters
Volume	15
Issue number	1
DOIs	https://doi.org/10.1016/0167-7152(92)90277-C
State	Published - Sep 3 1992

Keywords

Generalized linear model
nonparametric regression
score test

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10.1016/0167-7152(92)90277-C

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title = "Score tests in a generalized linear model with surrogate covariates",

abstract = "We consider generalized linear models where a predictor is measured with error. The efficient score test for the effect of that predictor depends on the regression of the true predictor on its observed surrogate. Using validation data, we estimate the regression by nonparametric techniques. The resulting semiparametric score test is shown to be nearly asymptotically efficient.",

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PY - 1992/9/3

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N2 - We consider generalized linear models where a predictor is measured with error. The efficient score test for the effect of that predictor depends on the regression of the true predictor on its observed surrogate. Using validation data, we estimate the regression by nonparametric techniques. The resulting semiparametric score test is shown to be nearly asymptotically efficient.

AB - We consider generalized linear models where a predictor is measured with error. The efficient score test for the effect of that predictor depends on the regression of the true predictor on its observed surrogate. Using validation data, we estimate the regression by nonparametric techniques. The resulting semiparametric score test is shown to be nearly asymptotically efficient.

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KW - nonparametric regression

KW - score test

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Score tests in a generalized linear model with surrogate covariates

Abstract

Keywords

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