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Testing goodness of fit of polynomial models via spline smoothing techniques


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  • Chen, Juei-Chao
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    A new test statistic is proposed for testing goodness of fit of an mth order polynomial regression model. The test statistic is [integral operator]10[[mu](m)[lambda](t)]2 dt, where [mu](m)[lambda] is the mth order derivative of a 2mth order smoothing spline estimator for the regression function [mu] and [lambda] is its associated smoothing parameter. The large sample properties of the test statistic are derived under both the null hypothesis and local alternatives. A numerical example is included that illustrates the technique.

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 19 (1994)
    Issue (Month): 1 (January)
    Pages: 65-76

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    Handle: RePEc:eee:stapro:v:19:y:1994:i:1:p:65-76

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    Keywords: Asymptotic distribution local alternatives nonparametric regression polynomial regression;


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    Cited by:
    1. Emmanuel Guerre & Pascal Lavergne, 2001. "Rate-optimal data-driven specification testing in regression models," Econometrics 0107001, EconWPA.
    2. Chin-Shang Li, 2012. "Testing for no effect via splines," Computational Statistics, Springer, vol. 27(2), pages 343-357, June.
    3. Emmanuel Guerre & Pascal Lavergne, 2004. "Data-Driven Rate-Optimal Specification Testing In Regression Models," Econometrics 0411008, EconWPA.


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