English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper considers errors-in-variables models in a high-dimensional settingwhere the number of covariates can be much larger than the sample size, andthere are only a small number of non-zero covariates. The presence ofmeasurement error in the covariates can result in severely biased parameterestimates, and also affects the ability of penalized methods such as the lassoto recover the true sparsity pattern. A new estimation procedure calledSIMSELEX (SIMulation-SELection-EXtrapolation) is proposed. This procedureaugments the traditional SIMEX approach with a variable selection step based onthe group lasso. The SIMSELEX estimator is shown to perform well in variableselection, and has significantly lower estimation error than naive estimatorsthat ignore measurement error. SIMSELEX can be applied in a variety oferrors-in-variables settings, including linear models, generalized linearmodels, and Cox survival models. It is furthermore shown how SIMSELEX can beapplied to spline-based regression models. SIMSELEX estimators are compared tothe corrected lasso and the conic programming estimator for a linear model, andto the conditional scores lasso for a logistic regression model. Finally, themethod is used to analyze a microarray dataset that contains gene expressionmeasurements of favorable histology Wilms tumors.

Simulation-Selection-Extrapolation: Estimation in High-Dimensional Errors-in-Variables Models