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Activity Number: 310 - Advances and Novel Problems in Flexible Analysis of Clustered Data with Complex Structures
Type: Invited
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #322172 View Presentation
Title: Flexible parametric approach to classical measurement error variance estimation without auxiliary data
Author(s): Ingrid Van Keilegom*
Keywords: survival analysis ; Bernstein polynomials ; proportional hazards

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. In this paper, 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.

Authors who are presenting talks have a * after their name.

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