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Efficient Local Estimation for Time-Varying Coefficients in Deterministic Dynamic Models With Applications to HIV-1 Dynamics

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  • Chen, Jianwei
  • Wu, Hulin

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  • Chen, Jianwei & Wu, Hulin, 2008. "Efficient Local Estimation for Time-Varying Coefficients in Deterministic Dynamic Models With Applications to HIV-1 Dynamics," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 369-384, March.
    • Handle: RePEc:bes:jnlasa:v:103:y:2008:m:march:p:369-384
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    Citations

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    Cited by:

    1. J. Cao & L. Wang & J. Xu, 2011. "Robust Estimation for Ordinary Differential Equation Models," Biometrics, The International Biometric Society, vol. 67(4), pages 1305-1313, December.
    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. Tao Lu & Yangxin Huang & Min Wang & Feng Qian, 2014. "A refined parameter estimating approach for HIV dynamic model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1645-1657, August.
    4. Hong, Zhaoping & Lian, Heng, 2012. "Time-varying coefficient estimation in differential equation models with noisy time-varying covariates," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 58-67, January.
    5. Xinyu Zhang & Jiguo Cao & Raymond J. Carroll, 2015. "On the selection of ordinary differential equation models with application to predator-prey dynamical models," Biometrics, The International Biometric Society, vol. 71(1), pages 131-138, March.
    6. Xinyu Zhang & Jiguo Cao & Raymond J. Carroll, 2017. "Estimating varying coefficients for partial differential equation models," Biometrics, The International Biometric Society, vol. 73(3), pages 949-959, September.
    7. Yingdong Yin & Yupeng Xi & Cheng Xu & Qiwen Sun, 2022. "The Basic Reproduction Number and Delayed Action of T Cells for Patients Infected with SARS-CoV-2," Mathematics, MDPI, vol. 10(12), pages 1-18, June.
    8. Liu Baisen & Wang Liangliang & Cao Jiguo, 2018. "Bayesian estimation of ordinary differential equation models when the likelihood has multiple local modes," Monte Carlo Methods and Applications, De Gruyter, vol. 24(2), pages 117-127, June.
    9. Liu, Baisen & Wang, Liangliang & Nie, Yunlong & Cao, Jiguo, 2019. "Bayesian inference of mixed-effects ordinary differential equations models using heavy-tailed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 233-246.
    10. Zhang, Tingting & Sun, Yinge & Li, Huazhang & Yan, Guofen & Tanabe, Seiji & Miao, Ruizhong & Wang, Yaotian & Caffo, Brian S. & Quigg, Mark S., 2020. "Bayesian inference of a directional brain network model for intracranial EEG data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

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