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 explanatory variables to be measured with error. The structural approach, in which the covariates measured...

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Bibliographic Details
Published in:Journal of applied statistics 2014-07, Vol.41 (7), p.1530-1547
Main Authors: Carrasco, Jalmar M.F., Ferrari, Silvia L.P., Arellano-Valle, Reinaldo B.
Format: Article
Language:English
Subjects:
Applied statistics
beta regression model
Calibration
Computer simulation
Error analysis
errors-in-variables model
Estimators
Gauss-Hermite quadrature
Mathematical models
maximum likelihood
Maximum likelihood method
maximum pseudo-likelihood
Measurement errors
Monte Carlo simulation
Permissible error
Random variables
Regression
Regression analysis
regression calibration
Studies
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Summary: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 explanatory variables to be measured with error. The structural approach, in which the covariates measured with error are assumed to be random variables, is employed. Three estimation methods are presented, namely maximum likelihood, maximum pseudo-likelihood and regression calibration. Monte Carlo simulations are used to evaluate the performance of the proposed estimators and the naïve estimator. Also, a residual analysis for beta regression models with measurement errors is proposed. The results are illustrated in a real data set.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2014.881784