# PSEUDO-LIKELIHOOD INFERENCE FOR REGRESSION MODELS WITH MISCLASSIFIED AND MISMEASURED VARIABLES

@article{Guolo2011PSEUDOLIKELIHOODIF, title={PSEUDO-LIKELIHOOD INFERENCE FOR REGRESSION MODELS WITH MISCLASSIFIED AND MISMEASURED VARIABLES}, author={Annamaria Guolo}, journal={Statistica Sinica}, year={2011}, volume={21}, pages={1639-1663} }

This paper investigates the use of a pseudo-likelihood approach for infer- ence in regression models with covariates affected by measurement errors. The max- imum pseudo-likelihood estimator is obtained through a Monte Carlo expectation- maximization type algorithm and its asymptotic properties are described. The fi- nite sample performance of the pseudo-likelihood approach is investigated through simulation studies, and compared to a full likelihood approach and to regression calibration under…

## 7 Citations

Pseudo-likelihood and bootstrapped pseudo-likelihood inference in logistic regression model with misclassified responses

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A Monte Carlo EM algorithm is applied to estimate the parameters of the simplex regression model when there is measurement error in the covariate using a pseudo-likelihood function to investigate the impact of ignoring the measurement error.

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1 Regression modelling where explanatory variables are measured with error is a common prob- 2 lem in applied sciences. However, if inappropriate analysis methods are applied, then unreliable 3…

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This work deals with estimation and diagnostic analytics in regression modelling based on the Birnbaum–Saunders distribution using additive measurement errors using the maximum pseudo-likelihood and regression calibration methods for parameter estimation.

Inference from PseudoLikelihoods with Plug‐In Estimates

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Effective implementation of likelihood inference in models for high‐dimensional data often requires a simplified treatment of nuisance parameters, with these having to be replaced by handy estimates.…

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