We introduce a novel function-on-function linear quantile regression model to characterize the entire conditional distribution of a functional response for a given functional predictor. Tensor cubic B-splines expansion is used to represent the regression parameter functions, where a derivative-free ...
A general framework for smooth regression of a functional response on one or multiple functional predictors is proposed. Using the mixed model representation of penalized regression expands the scope of function-on-function regression to many realistic scenarios. In particular, the approach can accommodat...
dimensional data in this study, and we compared them using simulation and soil datasets. We discovered that grouping had a significant impact on model correctness and error reduction. For the core projection step, we first looked at the properties of all the algorithms and how they function to ...
This is a wrapper function for the penalized function of the well-established R package of the same name [5, 6]. A routine for conditional logistic regression is not directly available in penalized, but we exploit the fact that the likelihood of a conditional logistic regression model is the...
We consider a study on regression function estimation over a bounded domain of arbitrary shapes based on triangulation and penalization techniques. A total variation type penalty is imposed to encourage fusion of adjacent triangles, which leads to a partition of the domain consisting of disjointed poly...
Simulation studies and real world data serve for illustration and to compare the approaches to methods often seen in practice, namely simple linear regression on the group labels and pure dummy coding. Especially the proposed difference penalty turns out to be highly competitive. 展开 ...
The coordinate decent algorithm is a “one-at-a-time” approach40, and before considering the coordinate descent algorithm for the nonlinear logistic regularization, we first introduce a linear regression case. The objective function of the linear regression is as follow: $$min\left\{ {\frac{1...
asgl: A Python Package for Penalized Linear and Quantile Regression For a practical introduction to the package, users can refer to the user guide notebook available in the GitHub repository. Additional accessible explanations can be found onTowards Data Science: Sparse Group Lasso,Towards Data Scie...
We present a new method for variable selection and function estimation in non parametric additive logistic models fitted by cubic smoothing splines: penalized additive logistic regression. The method is based on a generalization of the lasso. Because of their nature, these constraints shrink linear ...
regression models. We introduce a group penalized pseudo quantile regression (GPQR) framework with both group-lasso and group non-convex penalties. We approximate the quantile regression check function using a pseudo-quantile check function. Then, using the majorization–minimization principle, we ...