It is known that the estimating equations for quantile regression (QR) can be solved using an EM algorithm in which M-step computed via weighted least squares, with weights at E-step as expectation of independent generalized inverse-Gaussian variables. This fact exploited here to extend QR allow random effects linear predictor. Convergence this setting established by showing it a alternating mi...

When Multicollinearity exists in a data set, the is considered deficient. frequently encountered observational studies. It creates difficulties when building regression models. phenomenon whereby two or more explanatory variable multiple model are highly correlated. Variable selection an important aspect of as such choice best subset among many variables to be included most difficult part analy...