EcoSta 2017: Start Registration
View Submission - EcoSta2017
A0279
Title: Flexible parametric approach to classical measurement error variance estimation without auxiliary data Authors:  Ingrid Van Keilegom - KU Leuven (Belgium) [presenting]
Aurelie Bertrand - Universite catholique de Louvain (Belgium)
Catherine Legrand - Universite Catholique de Louvain (Belgium)
Abstract: Measurement error in the continuous covariates of a model generally yields bias in the estimators. It is a frequent problem in practice, and many correction procedures have been developed for different classes of models. However, in most cases, some information about the measurement error distribution is required. When neither validation nor auxiliary data (e.g., replicated measurements) is available, this specification turns out to be tricky. We develop a likelihood-based procedure to estimate the variance of classical additive error of Gaussian distribution, without additional information. The performance of this estimator is investigated both in an asymptotic way and through finite-sample simulations. The usefulness of the obtained estimator when using the Simulation-Extrapolation algorithm, a widely used correction method, is then analyzed in the Cox proportional hazards model through other simulations. Finally, the whole procedure is illustrated on real data.