"Conditional Dependence Models Under Covariate Measurement Error"

Speaker: Dr. Elif Acar, Department of Statistics

Thursday, November 28, 2019 in 111 Armes Lecture Building at 3:45pm

Refreshments will be served between 3:15pm-3:45pm in 318D Machray Hall before the seminar.



In many applications, covariates are subject to measurement error. While there is a vast literature

on measurement error problems in regression settings, very little is known about the impact of

covariate measurement error on the dependence parameter estimation in multivariate models. In

this work, we address the latter problem using a conditional copula model, and show that the

dependence parameter estimates can be significantly biased if the covariate measurement error is

ignored in the analysis. We identify the underlying bias pattern from the direction and magnitude

of marginal effect sizes and introduce an analytical bias correction method for the special case of

the Gaussian copula. For general conditional copula models, a likelihood-based correction

method is proposed, in which the likelihood function is computed via Monte-Carlo integration.

Numerical studies confirm that the proposed bias-correction methods achieve accurate estimation

of the dependence parameter.


Nov 21, 2019