0000000000243153

AUTHOR

Matteo Bottai

showing 3 related works from this author

A penalized approach to covariate selection through quantile regression coefficient models

2019

The coefficients of a quantile regression model are one-to-one functions of the order of the quantile. In standard quantile regression (QR), different quantiles are estimated one at a time. Another possibility is to model the coefficient functions parametrically, an approach that is referred to as quantile regression coefficients modeling (QRCM). Compared with standard QR, the QRCM approach facilitates estimation, inference and interpretation of the results, and generates more efficient estimators. We designed a penalized method that can address the selection of covariates in this particular modelling framework. Unlike standard penalized quantile regression estimators, in which model selec…

Statistics and Probability05 social sciencesQuantile regression model01 natural sciencesQuantile regressionInspiratory capacity010104 statistics & probabilitypenalized quantile regression coefficients modelling (QRCM p )Lasso penalty0502 economics and businessCovariateStatisticsPenalized integrated loss minimization (PILM)tuning parameter selection0101 mathematicsStatistics Probability and UncertaintySelection (genetic algorithm)050205 econometrics MathematicsQuantile
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Nonlinear parametric quantile models

2020

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 …

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 AnalysisQuantileStatistical Methods in Medical Research
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sj-zip-1-smm-10.1177_0962280220941159 - Supplemental material for Nonlinear parametric quantile models

2020

Supplemental material, sj-zip-1-smm-10.1177_0962280220941159 for Nonlinear parametric quantile models by Matteo Bottai and Giovanna Cilluffo in Statistical Methods in Medical Research

111099 Nursing not elsewhere classified111708 Health and Community Services160807 Sociological Methodology and Research MethodsFOS: Health sciencesFOS: Sociology
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