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Statistical diagnosis for HIV dynamics based on mean shift outlier model

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Abstract

Ordinary differential equation (ODE) are widely used for quantifying HIV viral dynamics. It is interesting but challenging to estimate ODE parameters from noisy data, especially when the data have some outliers. In this study, the authors use the Mean Shift Outlier Model (MSOM) to detect outliers in HIV model based on the two-step estimation of ODE. Approximate formula for shift parameter is derived. Furthermore, a score test statistic is constructed and its approximating distribution is established. The simulation results show that: 1) The boundary points have more impact on the parameter estimation relative to interior points. 2) The proposed procedure can detect the outliers effectively. The authors illustrate the proposed approach using an application example from an HIV clinical trial and find similar pattern to the simulation studies.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, China

    Ting Wu, Sanyang Liu & Jie Zhou

Authors
  1. Ting Wu
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  2. Sanyang Liu
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  3. Jie Zhou
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Corresponding author

Correspondence to Ting Wu.

Additional information

This research was supported by the Fundamental Research Funds for the Cental Universities under Grant No. JB140706 and the National Funds of China for Young Scientists under Grant Nos. 11302157 and 11301409.

This paper was recommended for publication by Editor LIU Yungang.

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Wu, T., Liu, S. & Zhou, J. Statistical diagnosis for HIV dynamics based on mean shift outlier model. J Syst Sci Complex 28, 592–605 (2015). https://doi.org/10.1007/s11424-015-4021-4

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  • DOI: https://doi.org/10.1007/s11424-015-4021-4

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