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M-type smoothing spline ANOVA for correlated data

Author

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  • Liu, Anna
  • Qin, Li
  • Staudenmayer, John

Abstract

This paper concerns outlier robust non-parametric regression with smoothing splines for data that are possibly correlated. We define a robust smoother as the minimizer of a penalized robustified log likelihood. Our estimation algorithm uses iteratively reweighted least squares to estimate the regression function. We develop two types of robust methods for joint estimation of the smoothing parameters and the correlation parameters: indirect methods and direct methods, terms borrowed from the related generalized smoothing spline literature. The indirect methods choose those parameters by conveniently approximating the distribution of the working data at each iteration as Gaussian. The direct methods estimate those parameters to minimize an estimate of the loss between the truth and the final estimated regression. Indirect methods are computationally more efficient, but our empirical studies suggest that direct methods result in more accurate estimates. Finally, the methods are applied to a data set from a macaque Simian-Human Immunodeficiency Virus (SHIV) challenge study.

Suggested Citation

  • Liu, Anna & Qin, Li & Staudenmayer, John, 2010. "M-type smoothing spline ANOVA for correlated data," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2282-2296, November.
    • Handle: RePEc:eee:jmvana:v:101:y:2010:i:10:p:2282-2296
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    References listed on IDEAS

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    1. He, Xuming & Fung, Wing K. & Zhu, Zhongyi, 2005. "Robust Estimation in Generalized Partial Linear Models for Clustered Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1176-1184, December.
    2. J. E. Mills & C. A. Field & D. J. Dupuis, 2002. "Marginally Specified Generalized Linear Mixed Models: A Robust Approach," Biometrics, The International Biometric Society, vol. 58(4), pages 727-734, December.
    3. John S. Preisser & Bahjat F. Qaqish, 1999. "Robust Regression for Clustered Data with Application to Binary Responses," Biometrics, The International Biometric Society, vol. 55(2), pages 574-579, June.
    4. You-Gan Wang & Xu Lin & Min Zhu, 2005. "Robust Estimating Functions and Bias Correction for Longitudinal Data Analysis," Biometrics, The International Biometric Society, vol. 61(3), pages 684-691, September.
    5. Guo You Qin & Zhong Yi Zhu, 2009. "Robustified Maximum Likelihood Estimation in Generalized Partial Linear Mixed Model for Longitudinal Data," Biometrics, The International Biometric Society, vol. 65(1), pages 52-59, March.
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