Approximate likelihoods for generalized linear errors-in-variables models

John J. Hanfelt, Kung Yee Liang

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


When measurement error is present in covariates, it is well known that naïvely fitting a generalized linear model results in inconsistent inferences. Several methods have been proposed to adjust for measurement error without making undue distributional assumptions about the unobserved true covariates. Stefanski and Carroll focused on an unbiased estimating function rather than a likelihood approach. Their estimating function, known as the conditional score, exists for logistic regression models but has two problems: a poorly behaved Wald test and multiple solutions. They suggested a heuristic procedure to identify the best solution that works well in practice but has little theoretical support compared with maximum likelihood estimation. To help to resolve these problems, we propose a conditional quasi-likelihood to accompany the conditional score that provides an alternative to Wald's test and successfully identifies the consistent solution in large samples.

Original languageEnglish (US)
Pages (from-to)627-637
Number of pages11
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number3
StatePublished - 1997
Externally publishedYes


  • Conditional Score
  • Measurement Error
  • Multiple Roots
  • Quasi-Likelihood
  • Wald Test

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Approximate likelihoods for generalized linear errors-in-variables models'. Together they form a unique fingerprint.

Cite this