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Testing goodness of fit of polynomial models via spline smoothing techniques

Author

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  • Chen, Juei-Chao
Abstract
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.

Suggested Citation

  • Chen, Juei-Chao, 1994. "Testing goodness of fit of polynomial models via spline smoothing techniques," Statistics & Probability Letters, Elsevier, vol. 19(1), pages 65-76, January.
    • Handle: RePEc:eee:stapro:v:19:y:1994:i:1:p:65-76
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    Cited by:

    1. Emmanuel Guerre & Pascal Lavergne, 2001. "Rate-optimal data-driven specification testing in regression models," Econometrics 0107001, University Library of Munich, Germany.
    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, University Library of Munich, Germany.

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