{"title":"Non compact estimation of the conditional density from direct or noisy data","authors":"F. Comte, C. Lacour","doi":"10.1214/22-aihp1291","DOIUrl":null,"url":null,"abstract":". In this paper, we propose a nonparametric estimation strategy for the conditional density function of Y given X , from independent and identically distributed observations ( X i , Y i ) 1 ≤ i ≤ n . We consider a regression strategy related to projection subspaces of L 2 generated by non compactly supported bases. This (cid:28)rst study is then extended to the case where Y is not directly observed, but only Z = Y + ε , where ε is a noise with known density. In these two settings, we build and study collections of estimators, compute their rates of convergence on anisotropic space on non-compact supports, and prove related lower bounds. Then, we consider adaptive estimators for which we also prove risk bounds.","PeriodicalId":42884,"journal":{"name":"Annales de l Institut Henri Poincare D","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annales de l Institut Henri Poincare D","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1214/22-aihp1291","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MATHEMATICAL","Score":null,"Total":0}
引用次数: 2
Abstract
. In this paper, we propose a nonparametric estimation strategy for the conditional density function of Y given X , from independent and identically distributed observations ( X i , Y i ) 1 ≤ i ≤ n . We consider a regression strategy related to projection subspaces of L 2 generated by non compactly supported bases. This (cid:28)rst study is then extended to the case where Y is not directly observed, but only Z = Y + ε , where ε is a noise with known density. In these two settings, we build and study collections of estimators, compute their rates of convergence on anisotropic space on non-compact supports, and prove related lower bounds. Then, we consider adaptive estimators for which we also prove risk bounds.
. 本文针对给定X的独立同分布观测值(X i, Y i) 1≤i≤n,提出了Y的条件密度函数的非参数估计策略。我们考虑了一种由非紧支持基生成的l2的投影子空间的回归策略。这(cid:28)第一个研究然后被扩展到没有直接观察到Y,而只有Z = Y + ε的情况,其中ε是已知密度的噪声。在这两种情况下,我们建立和研究了估计量集合,计算了它们在非紧支撑上的各向异性空间上的收敛速率,并证明了相关的下界。然后,我们考虑自适应估计器,我们也证明了风险界限。