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A generalized correlated Cp criterion for derivative estimation with dependent errors

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  • Liu, Sisheng
  • Kong, Xiaoli

Abstract

In practice, it is common that errors are correlated for the nonparametric regression model. Although many methods have been developed for addressing correlated errors for tuning parameter selection to recover the mean response function, few studies have been proposed to select tuning parameters for derivative estimation. In this paper, a generalized correlated Cp (GCCp) criterion is proposed to choose a tuning parameter for derivative estimation in the presence of correlated errors. It can be applied for any nonparametric estimation linear in responses, including kernel regression, local regression, smoothing spline, etc. The GCCp criterion is justified both theoretically and empirically via simulation studies. Finally, an air quality index data example in Changsha city is provided to illustrate the application of the proposed criterion.

Suggested Citation

  • Liu, Sisheng & Kong, Xiaoli, 2022. "A generalized correlated Cp criterion for derivative estimation with dependent errors," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:csdana:v:171:y:2022:i:c:s0167947322000536
    DOI: 10.1016/j.csda.2022.107473
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    References listed on IDEAS

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    1. Byeong U. Park & Young Kyung Lee & Tae Yoon Kim & Cheolyong Park, 2006. "A Simple Estimator of Error Correlation in Non‐parametric Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(3), pages 451-462, September.
    2. Tae Yoon Kim, 2004. "Nonparametric detection of correlated errors," Biometrika, Biometrika Trust, vol. 91(2), pages 491-496, June.
    3. Jan Beran & Yuanhua Feng, 2002. "Local Polynomial Fitting with Long-Memory, Short-Memory and Antipersistent Errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(2), pages 291-311, June.
    4. Patrick Carmack & Jeffrey Spence & William Schucany, 2012. "Generalised correlated cross-validation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(2), pages 269-282.
    5. 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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    Cited by:

    1. Deru Kong & Shengli Zhao & WenWu Wang, 2024. "Nonparametric derivative estimation with bimodal kernels under correlated errors," Computational Statistics, Springer, vol. 39(4), pages 1847-1865, June.

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