Density deconvolution from repeated measurements without symmetry assumption on the errors

JOURNAL OF MULTIVARIATE ANALYSIS

卷:140

Article

Comte, Fabienne¹  Kappus, Johanna² 

[1] Univ Paris 05, UMR CNRS 8145, MAP 5, Paris, France

[2] Univ Rostock, Inst Math, D-18055 Rostock, Germany

关键词: Nonparametric estimation;    Density deconvolution;    Repeated measurements;    Panel data;   

DOI  :  10.1016/j.jmva.2015.04.004

来源: Elsevier

【 摘 要 】

We consider deconvolution from repeated observations with unknown error distribution. Until now, this model has mostly been studied under the additional assumption that the errors are symmetric. We construct an estimator for the non-symmetric error case and study its theoretical properties and practical performance. It is interesting to note that we can improve substantially upon the rates of convergence which have been presented in the literature and, at the same time, dispose of most of the extremely restrictive assumptions which have been imposed so far. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free