A semiparametric correction for attenuation

J. H. Sepanski, R. Knickerbocker, R. J. Carroll

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

A correction method is proposed for models including the generalized linear model when the covariate is measured with error. The method requires a separate validation data set that consists of the surrogate W and the true covariate X or an unbiased estimate X+of X. We do not require the classical additive measurement error model in which the surrogate is unbiased for the true covariates. We first obtain an estimate of E(X W) by using nonparametric kernel regression of X or X+on W based on the validation data. Then we perform a standard analysis with the unknown X replaced by the estimate of E(X W). The asymptotic distribution of the resulting regression parameter estimator is obtained. Generalizations to include components of X measured without error are also discussed.

Original languageEnglish
Pages (from-to)1366-1373
Number of pages8
JournalJournal of the American Statistical Association
Volume89
Issue number428
DOIs
StatePublished - Dec 1994

Keywords

  • Generalized linear model
  • Kernel regression
  • Measurement error models
  • Quasi-likelihood estimation

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