Search results for "Lasso penalty"
showing 2 items of 2 documents
Quantile Regression Coefficients Modeling: a Penalized Approach
2018
Modeling quantile regression coefficients functions permits describing the coefficients of a quantile regression model as parametric functions of the order of the quantile. This approach has numerous advantages over standard quantile regression, in which different quantiles are estimated one at the time: it facilitates estimation and inference, improves the interpretation of the results, and is statistically efficient. On the other hand, it poses new challenges in terms of model selection. We describe a penalized approach that can be used to identify a parsimonious model that can fit the data well. We describe the method, and analyze the dataset that motivated the present paper. The propose…
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…