The distribution of a linear predictor after model selection: Unconditional finite-sample distributions and asymptotic approximations

Simple item page
dc.creatorLeeb, Hannes
dc.date2006-11-07
dc.date.accessioned2026-07-07T12:07:20Z
dc.date.available2026-07-07T12:07:20Z
dc.descriptionWe analyze the (unconditional) distribution of a linear predictor that is constructed after a data-driven model selection step in a linear regression model. First, we derive the exact finite-sample cumulative distribution function (cdf) of the linear predictor, and a simple approximation to this (complicated) cdf. We then analyze the large-sample limit behavior of these cdfs, in the fixed-parameter case and under local alternatives.
dc.descriptionPublished at http://dx.doi.org/10.1214/074921706000000518 in the IMS Lecture Notes--Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0611186
dc.identifierhttp://arxiv.org/abs/math/0611186
dc.identifierIMS Lecture Notes--Monograph Series 2006, Vol. 49, 291-311
dc.identifierdoi:10.1214/074921706000000518
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/208935
dc.subjectStatistics Theory
dc.subjectStatistical Finance
dc.subject62E15 (Primary) 62F10, 62F12, 62J05 (Secondary)
dc.titleThe distribution of a linear predictor after model selection: Unconditional finite-sample distributions and asymptotic approximations
dc.typetext

Files

Collections

arXiv