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Inference on Distribution Functions under Measurement Erro

主讲人: Yoon-Jae Whang
主讲人简介:

Upload/File/2018/12/20181216070004908.pdf

主持人: Qingliang Fan
简介:

This paper is concerned with inference on the cumulative distribution function (cdf)

FX in the classical measurement error model X = X + . We consider the case where the density of the measurement error  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  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  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.
时间: 2018-12-21(Friday)16:40-18:00
地点: D236, Econ Building
期数: 高级计量经济学与统计学系列讲座2018秋季学期第
类型: 系列讲座
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