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

In this paper we consider the conservative Lasso which we argue penalizesmore correctly than the Lasso and show how it may be desparsified in the senseof van de Geer et al. (2014) in order to construct asymptotically honest(uniform) confidence bands. In particular, we develop an oracle inequality forthe conservative Lasso only assuming the existence of a certain number ofmoments. This is done by means of the Marcinkiewicz-Zygmund inequality. Weallow for heteroskedastic non-subgaussian error terms and covariates. Next, wedesparsify the conservative Lasso estimator and derive the asymptoticdistribution of tests involving an increasing number of parameters. Oursimulations reveal that the desparsified conservative Lasso estimates theparameters more precisely than the desparsified Lasso, has better sizeproperties and produces confidence bands with superior coverage rates.

Asymptotically Honest Confidence Regions for High Dimensional Parameters by the Desparsified Conservative Lasso