Modelling and estimation of nonlinear quantile regression with clustered data.

ABSTRACT: In regression applications, the presence of nonlinearity and correlation among observations offer computational challenges not only in traditional settings such as least squares regression, but also (and especially) when the objective function is nonsmooth as in the case of quantile regression. Methods are developed for the modelling and estimation of nonlinear conditional quantile functions when data are clustered within two-level nested designs. The proposed estimation algorithm is a blend of a smoothing algorithm for quantile regression and a second order Laplacian approximation for nonlinear mixed models. This optimization approach has the appealing advantage of reducing the original nonsmooth problem to an approximated L ₂ problem. While the estimation algorithm is i

SUBMITTER: Geraci M 

PROVIDER: S-EPMC6663105 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature