The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Giovanna Cilluffo, Gianluca Sottile, Stefania La Grutta, Vito M R Muggeo
Journal/Conference Name Statistical methods in medical research
Paper Category
Paper Abstract This paper focuses on hypothesis testing in lasso regression, when one is interested in judging statistical significance for the regression coefficients in the regression equation involving a lot of covariates. To get reliable p-values, we propose a new lasso-type estimator relying on the idea of induced smoothing which allows to obtain appropriate covariance matrix and Wald statistic relatively easily. Some simulation experiments reveal that our approach exhibits good performance when contrasted with the recent inferential tools in the lasso framework. Two real data analyses are presented to illustrate the proposed framework in practice.
Date of publication 2019
Code Programming Language R
Comment

Copyright Researcher 2021