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Testing Heteroscedasticity in Nonparametric Regression Based on Trend Analysis

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  • Si-Lian Shen
  • Jian-Ling Cui
  • Chun-Wei Wang

Abstract

We first propose in this paper a new test method for detecting heteroscedasticity of the error term in nonparametric regression. Some simulation experiments are then conducted to evaluate the performance of the proposed methodology. A real‐world data set is finally analyzed to demonstrate the application of the method.

Suggested Citation

  • Si-Lian Shen & Jian-Ling Cui & Chun-Wei Wang, 2014. "Testing Heteroscedasticity in Nonparametric Regression Based on Trend Analysis," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:435925
    DOI: 10.1155/2014/435925
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    References listed on IDEAS

    as
    1. H. Dette & A. Munk, 1998. "Testing heteroscedasticity in nonparametric regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(4), pages 693-708.
    2. You, Jinhong & Chen, Gemai & Zhou, Yong, 2007. "Statistical inference of partially linear regression models with heteroscedastic errors," Journal of Multivariate Analysis, Elsevier, vol. 98(8), pages 1539-1557, September.
    3. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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