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)
    4 Downloads (Pure)

    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)833-900
    Number of pages68
    JournalAnnals of Applied Statistics
    Volume9
    Issue number2
    DOIs
    Publication statusPublished - 2015

    Fingerprint

    Errors in Variables
    Tax
    Taxation
    Measurement errors
    Measurement Error
    Jump
    Health
    Regression
    Predictors
    Health insurance
    Errors-in-variables Model
    Nonparametric Regression
    Regression Function
    Insurance
    Justification
    Numerical Study
    Discontinuity
    Regression Model
    Estimator
    Unknown

    Cite this

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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.",
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    Jump Detection in Generalized Error-in-Variables Regression with an Application to Australian Health Tax Policies. / kang, yicheng; GONG, Xiaodong; Gao, Jita; Peihua, Qiu.

    In: Annals of Applied Statistics, Vol. 9, No. 2, 2015, p. 833-900.

    Research output: Contribution to journalArticle

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    AU - Gao, Jita

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