title: Parametric modeling of quantile regression coefficient functions.
abstract: Estimating the conditional quantiles of outcome variables of interest is frequent in many research areas, and quantile regression is foremost among the utilized methods. The coefficients of a quantile regression model depend on the order of the quantile being estimated. For example, the coefficients for the median are generally different from those of the 10th centile. In this article, we describe an approach to modeling the regression coefficients as parametric functions of the order of the quantile. This approach may have advantages in terms of parsimony, efficiency, and may expand the potential of statistical modeling. Goodness-of-fit measures and testing procedures are discussed, and the results of a simulation study are presented. We apply the method to analyze the data that motivated this work. The described method is implemented in the qrcm R package.
authors.affiliation: Karolinska Institutet, Institute of Environmental Medicine, Unit of Biostatistics, Nobels väg 13, 171 77 Stockholm, Sweden.
authors.forename: Paolo
authors.initials: P
authors.lastname: Frumento
authors.affiliation: Karolinska Institutet, Institute of Environmental Medicine, Unit of Biostatistics, Nobels väg 13, 171 77 Stockholm, Sweden.
authors.forename: Matteo
authors.initials: M
authors.lastname: Bottai
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software
keywords: Inspiratory capacity; Integrated loss minimization (ILM); Quantile regression coefficients modeling (QRCM)