Showing 6 of total 6 results (show query)
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qrcm:Quantile Regression Coefficients Modeling
Parametric modeling of quantile regression coefficient functions.
Maintained by Paolo Frumento. Last updated 1 years ago.
1.78 score 2 dependents
gianluca-sottile
qrcmNP:Nonlinear and Penalized Quantile Regression Coefficients Modeling
Nonlinear and Penalized parametric modeling of quantile regression coefficient functions. Sottile G, Frumento P, Chiodi M and Bottai M (2020) <doi:10.1177/1471082X19825523>.
Maintained by Gianluca Sottile. Last updated 1 years ago.
1 stars 1.11 score 13 scriptsgianluca-sottile
clustEff:Clusters of Effects Curves in Quantile Regression Models
Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) <doi:10.1007/s00180-018-0817-8>.
Maintained by Gianluca Sottile. Last updated 1 years ago.
1.00 score 7 scriptscran
Mqrcm:M-Quantile Regression Coefficients Modeling
Parametric modeling of M-quantile regression coefficient functions.
Maintained by Paolo Frumento. Last updated 1 years ago.
1.00 scorecran
ctqr:Censored and Truncated Quantile Regression
Estimation of quantile regression models for survival data.
Maintained by Paolo Frumento. Last updated 1 years ago.
1 stars 1.00 scoregianluca-sottile
Qest:Quantile-Based Estimator
Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.
Maintained by Gianluca Sottile. Last updated 1 years ago.
1.00 score