Jump Detection in Generalized Error-in-Variables Regression with an Application to Australian Health Tax Policies

yicheng kang, Xiaodong GONG, Jita Gao, Qiu Peihua

    Research output: Contribution to journalArticle

    3 Citations (Scopus)
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    Abstract

    Without measurement errors in predictors, discontinuity of a non- parametric regression function at unknown locations could be esti- mated 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.
    Original languageEnglish
    Pages (from-to)883-900
    Number of pages18
    JournalAnnals of Applied Statistics
    Volume9
    Issue number2
    DOIs
    Publication statusPublished - 2015

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