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Department seminar by Lixing Zhu, Hong Kong Baptist UniversityExport this event to calendar

Thursday, August 9, 2018 — 4:00 PM EDT

An Adaptive-to-Model Test for Parametric Single-Index Errors-in-Variables Models


This seminar talks about some useful tests for fitting a parametric single-index regression model when covariates are measured with error and validation data is available. We propose  two tests whose consistency rates do not depend on the dimension of the covariate vector when an adaptive-to-model strategy is applied. One of these tests has  a bias term that becomes arbitrarily large with increasing sample size but its asymptotic variance is smaller, and the other is asymptotically unbiased with larger asymptotic variance. Compared with the existing local smoothing tests, the new tests behave like a classical local smoothing  test with only one covariate, and still are omnibus against general alternatives.

This avoids the difficulty associated with the curse of dimensionality.

Further, a systematic study is conducted to give an insight on the effect of the values of the ratio between the sample size and the size of validation data on the asymptotic behavior of these tests. Simulations are conducted to examine the performance in several finite sample scenarios.

Location 
M3 - Mathematics 3
Room: 3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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