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Detection of influential measurement for ordinary differential equation with application to HIV dynamics

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  • Zhou, Jie

Abstract

The detection of influential measurement is considered when ODE model is estimated by two-step approach. Two kinds of diagnosis approaches are proposed which are based on case deletion model and perturbation model respectively. The formulas for both diagnosis statistics turn out to have the same form. This fact can facilitate the implementation of the proposed diagnosis inferences. Numerical studies validate the efficacy of the proposed diagnosis approaches.

Suggested Citation

  • Zhou, Jie, 2015. "Detection of influential measurement for ordinary differential equation with application to HIV dynamics," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 324-332.
    • Handle: RePEc:eee:stapro:v:107:y:2015:i:c:p:324-332
    DOI: 10.1016/j.spl.2015.09.007
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    References listed on IDEAS

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    1. Jiguo Cao & James Ramsay, 2007. "Parameter cascades and profiling in functional data analysis," Computational Statistics, Springer, vol. 22(3), pages 335-351, September.
    2. Hulin Wu & Hongqi Xue & Arun Kumar, 2012. "Numerical Discretization-Based Estimation Methods for Ordinary Differential Equation Models via Penalized Spline Smoothing with Applications in Biomedical Research," Biometrics, The International Biometric Society, vol. 68(2), pages 344-352, June.
    3. Liang, Hua & Wu, Hulin, 2008. "Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1570-1583.
    4. J. O. Ramsay & G. Hooker & D. Campbell & J. Cao, 2007. "Parameter estimation for differential equations: a generalized smoothing approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 741-796, November.
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