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June 2015 Jump detection in generalized error-in-variables regression with an application to Australian health tax policies
Yicheng Kang, Xiaodong Gong, Jiti Gao, Peihua Qiu
Ann. Appl. Stat. 9(2): 883-900 (June 2015). DOI: 10.1214/15-AOAS814

Abstract

Without measurement errors in predictors, discontinuity of a nonparametric regression function at unknown locations could be estimated using a number of existing approaches. However, it becomes a challenging problem when the predictors contain measurement errors. In this paper, an error-in-variables jump point estimator is suggested for a nonparametric generalized error-in-variables regression model. A major feature of our method is that it does not impose any parametric distribution on the measurement error. Its performance is evaluated by both numerical studies and theoretical justifications. The method is applied to studying the impact of Medicare Levy Surcharge on the private health insurance take-up rate in Australia.

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Yicheng Kang. Xiaodong Gong. Jiti Gao. Peihua Qiu. "Jump detection in generalized error-in-variables regression with an application to Australian health tax policies." Ann. Appl. Stat. 9 (2) 883 - 900, June 2015. https://doi.org/10.1214/15-AOAS814

Information

Received: 1 October 2014; Revised: 1 February 2015; Published: June 2015
First available in Project Euclid: 20 July 2015

zbMATH: 06499935
MathSciNet: MR3371340
Digital Object Identifier: 10.1214/15-AOAS814

Keywords: Bandwidth selection , demand for private health insurance , exponential family , generalized regression , kernel smoothing , Measurement errors

Rights: Copyright © 2015 Institute of Mathematical Statistics

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Vol.9 • No. 2 • June 2015
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