Open Collections

Least productive relative error criterion based estimating equation approaches for the error-in-covariables multiplicative regression models

Banff International Research Station for Mathematical Innovation and Discovery

In this paper, we propose two estimating equation based methods to estimate the regression parameter vector in the multiplicative regression model when a subset of covariates are subject to measurement error but replicate measurements of their surrogates are available. Both methods allow the number of replicate measurements to vary between subjects. No parametric assumption is imposed on the measurement error term and the true covariates which are not observed in the data set. Under some regular- ity conditions, the asymptotic normality is established for both methods. Some simulation studies are conducted to assess the performances of the proposed methods. Real data analysis is used to illustrate our methods.

Mathematics; Statistics; Probability theory and stochastic processes

video/mp4

Author affiliation: Chinese Academy of Sciences

2017-02-17

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/60616

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/