6533b862fe1ef96bd12c76db
RESEARCH PRODUCT
Nonlinear parametric quantile models
Matteo BottaiGiovanna Cilluffosubject
Statistics and ProbabilityStatistics::Theoryquantile regressionEpidemiologyparametric010501 environmental sciences01 natural sciencesquantile regression coefficients models010104 statistics & probabilityOutcome variableHealth Information ManagementCovariateEconometricsHumansStatistics::MethodologyComputer Simulation0101 mathematicsChild0105 earth and related environmental sciencesParametric statisticsMathematicsModels StatisticalForced oscillation technique integrated loss function parametric quantile regression quantile regression coefficients models Child Computer Simulation Humans Regression Analysis Models Statistical Nonlinear DynamicsStatistics::ComputationQuantile regressionNonlinear systemNonlinear Dynamicsintegrated loss functionRegression AnalysisQuantiledescription
Quantile regression is widely used to estimate conditional quantiles of an outcome variable of interest given covariates. This method can estimate one quantile at a time without imposing any constraints on the quantile process other than the linear combination of covariates and parameters specified by the regression model. While this is a flexible modeling tool, it generally yields erratic estimates of conditional quantiles and regression coefficients. Recently, parametric models for the regression coefficients have been proposed that can help balance bias and sampling variability. So far, however, only models that are linear in the parameters and covariates have been explored. This paper presents the general case of nonlinear parametric quantile models. These can be nonlinear with respect to the parameters, the covariates, or both. Some important features and asymptotic properties of the proposed estimator are described, and its finite-sample behavior is assessed in a simulation study. Nonlinear parametric quantile models are applied to estimate extreme quantiles of longitudinal measures of respiratory mechanics in asthmatic children from an epidemiological study and to evaluate a dose–response relationship in a toxicological laboratory experiment.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 | Statistical Methods in Medical Research |