Inference on distribution functions under measurement error

Karun Adusumilli, Daisuke Kurisu, Taisuke Otsu, Yoon Jae Whang

研究成果: Article査読

4 被引用数 (Scopus)

抄録

This paper is concerned with inference on the cumulative distribution function (cdf) FX in the classical measurement error model X=X+ϵ. We consider the case where the density of the measurement error ϵ is unknown and estimated by repeated measurements, and show validity of a bootstrap approximation for the distribution of the deviation in the sup-norm between the deconvolution cdf estimator and FX . We allow the density of ϵ to be ordinary or super smooth. We also provide several theoretical results on the bootstrap and asymptotic Gumbel approximations of the sup-norm deviation for the case where the density of ϵ is known. Our approximation results are applicable to various contexts, such as confidence bands for FX and its quantiles, and for performing various cdf-based tests such as goodness-of-fit tests for parametric models of X, two sample homogeneity tests, and tests for stochastic dominance. Simulation and real data examples illustrate satisfactory performance of the proposed methods.

本文言語English
ページ(範囲)131-164
ページ数34
ジャーナルJournal of Econometrics
215
1
DOI
出版ステータスPublished - 2020 3
外部発表はい

ASJC Scopus subject areas

  • Economics and Econometrics

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