6533b839fe1ef96bd12a5c33
RESEARCH PRODUCT
The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression.
Giovanna CilluffoStefania La GruttaVito Mr MuggeoGianluca Sottilesubject
Statistics and ProbabilityStatistics::TheoryInduced smoothingEpidemiologyComputer scienceFeature selectionWald test01 natural sciencesasthma researchStatistics::Machine Learning010104 statistics & probability03 medical and health sciencesHealth Information ManagementLasso (statistics)Linear regressionsparse modelsStatistics::MethodologyComputer Simulation0101 mathematicssandwich formula030304 developmental biologyStatistical hypothesis testing0303 health sciencesCovariance matrixlung functionRegression analysisStatistics::Computationsparse modelResearch DesignAlgorithmSmoothingvariable selectiondescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 | Statistical methods in medical research |