A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection
We describe a simple, efficient, permutation based procedure for selecting the penalty parameter in the LASSO. The procedure, which is intended for applications where variable selection is the primary focus, can be applied in a variety of structural settings, including generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of three real data sets in which permutation selection is compared with cross-validation (CV), the Bayesian information criterion (BIC), and a selection method based on recently developed testing procedures for the LASSO.
Code (0)
등록된 구현이 없습니다.
Tasks
Model SelectionVariable SelectionSimilar Papers 제목 키워드 기반
Selecting Diverse Models for Scientific Insight
Model selection often aims to choose a single model, assuming that the form of the model is correct. However, there may be multiple possible underlying explanatory patterns in a set of predictors that could explain a res…
FormModel SelectionregressionVariable SelectionAPPLE: Approximate Path for Penalized Likelihood Estimators
In high-dimensional data analysis, penalized likelihood estimators are shown to provide superior results in both variable selection and parameter estimation. A new algorithm, APPLE, is proposed for calculating the Approx…
parameter estimationVariable SelectionTuning parameter selection in high dimensional penalized likelihood
Determining how to appropriately select the tuning parameter is essential in penalized likelihood methods for high-dimensional data analysis. We examine this problem in the setting of penalized likelihood methods for gen…
Vocal Bursts Intensity PredictionDifferentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases
Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving …
Privacy PreservingregressionAn Improved Online Penalty Parameter Selection Procedure for $\ell_1$-Penalized Autoregressive with Exogenous Variables
Many recent developments in the high-dimensional statistical time series literature have centered around time-dependent applications that can be adapted to regularized least squares. Of particular interest is the lasso, …
feature selectionTime SeriesTime Series Analysis