README.md
In ardeeshany/AFSSEN: Adaptive Function on Scalar Elastic Net

output: pdf_document: default

AFSSEN

Adaptive Function-on-Scalar Smoothing Elastic Net

AFSSEN is a methodology that simultaneously select significant predicotrs and produce smooth estimates of their parameters in a function-on-scalar linear modelwith sub-Gaussian errors and high-dimensional predictors.

For installing this package, use

devtools::install_github("ardeeshany/AFSSEN")

AFSSEN.R

We have option to control sparsity and smoothness separately with using two penalty parameters $\lambda_H$ and $\lambda_K$. We aim to estimate a smooth version of $\bf{\beta}$ to minimize the following target function.

$L_{\lambda}(\beta)=\dfrac{1}{2N} \|\bf{Y}-\bf{X}\bf{\beta}\|_{\mathbb{H}}^2+\frac{\lambda_{K}}{2} \sum_{i=1}^{I} \|L(\beta_{i})\|_{\mathbb{K}}^2 + \lambda_{H} \sum_{i=1}^{I} \tilde{w}_{i} \| \beta_{i}\|_{\mathbb{H}}$