The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 9, Number 2 (2015), 883-900.
Jump detection in generalized error-in-variables regression with an application to Australian health tax policies
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.
Ann. Appl. Stat., Volume 9, Number 2 (2015), 883-900.
Received: October 2014
Revised: February 2015
First available in Project Euclid: 20 July 2015
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Kang, Yicheng; Gong, Xiaodong; Gao, Jiti; Qiu, Peihua. Jump detection in generalized error-in-variables regression with an application to Australian health tax policies. Ann. Appl. Stat. 9 (2015), no. 2, 883--900. doi:10.1214/15-AOAS814. https://projecteuclid.org/euclid.aoas/1437397116
- Supplement to “Jump detection in generalized error-in-variables regression with an application to Australian health tax policies”. This supplemental file mainly gives the proof of Theorem 1.