Year: 2016 Vol.: 65 No.: 2
Authors: May Ann S. Estoy and Joseph Ryan G. Lansangan
Abstract:
Quantile regression and restricted maximum likelihood are incorporated into a backfitting approach to estimate a linear mixed model for clustered data. Simulation studies covering a wide variety of scenarios relating to clustering, presence of outliers, and model specification error are conducted to assess the performance of the proposed methods. The methods yield biased estimates yet high predictive ability compared to ordinary least squares and ordinary quantile regression.
Keywords: linear mixed models; quantile regression; restricted maximum likelihood; backfitting; bootstrap; clustered data