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}
}
  • A. Guolo
  • Published 1 October 2011
  • Mathematics
  • Statistica Sinica
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… 

Tables from this paper

Pseudo-likelihood and bootstrapped pseudo-likelihood inference in logistic regression model with misclassified responses
Logistic regression is an extensively used regression model for binary responses. In many applications, misclassification of binary responses is not uncommon. If the misclassification is ignored, it
Errors-in-variables beta regression models
Beta regression models provide an adequate approach for modeling continuous outcomes limited to the interval (0, 1). This paper deals with an extension of beta regression models that allow for
Simplex regression models with measurement error
TLDR
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.
Measurement error modeling in regression models with IRT measures as covariates
This contribution summarizes some techniques to adjust for the effect of the measurement error introduced when a latent variable measured by means of an IRT model is inserted as a covariate in a
An errors-in-variables model based on the Birnbaum-Saunders and its diagnostics with an application to earthquake data
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
An errors-in-variables model based on the Birnbaum–Saunders distribution and its diagnostics with an application to earthquake data
TLDR
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
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.

References

SHOWING 1-10 OF 28 REFERENCES
Likelihood Analysis and Flexible Structural Modeling for Measurement Error Model Regression
A computational approach is presented for likelihood analysis of regression models with measurement errors in explanatory variables. If y, x, and w represent the response, an unobservable true value
Segmented regression with errors in predictors: semi-parametric and parametric methods.
TLDR
It is shown that in threshold regression, the functional and structural methods differ substantially in their performance, and approximately consistent functional estimates can be as much as 25 times more variable than the maximum likelihood estimate for a properly specified parametric model.
A Semiparametric Mixture Approach to Case-Control Studies with Errors in Covariables
Abstract Methods are devised for estimating the parameters of a prospective logistic model in a case-control study with dichotomous response D that depends on a covariate X. For a portion of the
Maximum likelihood, multiple imputation and regression calibration for measurement error adjustment
TLDR
Three methods of estimation for main study/validation study design of exposure-disease association are discussed: maximum likelihood (ML), multiple imputation (MI) and regression calibration (RC), which indicate that with large measurement error or large enough sample sizes, ML performs as well as or better than MI and RC.
Mixture models in measurement error problems, with reference to epidemiological studies
Summary. The paper focuses on a Bayesian treatment of measurement error problems and on the question of the specification of the prior distribution of the unknown covariates. It presents a flexible
Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm
Two new implementations of the EM algorithm are proposed for maximum likelihood fitting of generalized linear mixed models. Both methods use random (independent and identically distributed) sampling
Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error.
TLDR
Two methods are provided to correct relative risk estimates obtained from logistic regression models for measurement errors in continuous exposures within cohort studies that may be due to either random (unbiased) within-person variation or to systematic errors for individual subjects.
Comparing the effects of continuous and discrete covariate mismeasurement, with emphasis on the dichotomization of mismeasured predictors.
TLDR
This work compares the bias induced by measurement error in a continuous predictor with that induced by misclassification of a binary predictor in the contexts of linear and logistic regression and gives a scenario where dichotomization involves a trade-off between model fit and mis classification bias.
Pseudo Maximum Likelihood Estimation: Theory and Applications
Abstract : Pseudo maximum likelihood estimation easily extends to k parameter models, and is of interest in problems in which the likelihood surface is ill-behaved in higher dimensions but
A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms
TLDR
Two modifications to the MCEM algorithm (the poor man's data augmentation algorithms), which allow for the calculation of the entire posterior, are presented and serve as diagnostics for the validity of the posterior distribution.
...
...