IDEAS home Printed from https://ideas.repec.org/r/bla/biomet/v62y2006i2p413-423.html
   My bibliography  Save this item

Hierarchical Bayesian Methods for Estimation of Parameters in a Longitudinal HIV Dynamic System

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Hanze Zhang & Yangxin Huang, 2020. "Quantile regression-based Bayesian joint modeling analysis of longitudinal–survival data, with application to an AIDS cohort study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(2), pages 339-368, April.
  2. Jeremie Guedj & Rodolphe Thiébaut & Daniel Commenges, 2011. "Joint Modeling of the Clinical Progression and of the Biomarkers' Dynamics Using a Mechanistic Model," Biometrics, The International Biometric Society, vol. 67(1), pages 59-66, March.
  3. Jianwei Chen, 2010. "Modelling long‐term human immunodeficiency virus dynamic models with application to acquired immune deficiency syndrome clinical study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 805-820, November.
  4. Lu, Xiaosun & Huang, Yangxin & Zhu, Yiliang, 2016. "Finite mixture of nonlinear mixed-effects joint models in the presence of missing and mismeasured covariate, with application to AIDS studies," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 119-130.
  5. Dagne Getachew & Huang Yangxin, 2012. "Bayesian inference for a nonlinear mixed-effects Tobit model with multivariate skew-t distributions: application to AIDS studies," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-24, September.
  6. J. Guedj & R. Thiébaut & D. Commenges, 2007. "Maximum Likelihood Estimation in Dynamical Models of HIV," Biometrics, The International Biometric Society, vol. 63(4), pages 1198-1206, December.
  7. Zhou, Jie & Han, Lu & Liu, Sanyang, 2013. "Nonlinear mixed-effects state space models with applications to HIV dynamics," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1448-1456.
  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. Hanwen Huang, 2022. "Bayesian multi‐level mixed‐effects model for influenza dynamics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1978-1995, November.
  10. Dacheng Liu & Tao Lu & Xu-Feng Niu & Hulin Wu, 2011. "Mixed-Effects State-Space Models for Analysis of Longitudinal Dynamic Systems," Biometrics, The International Biometric Society, vol. 67(2), pages 476-485, June.
  11. Huang, Yangxin, 2008. "Long-term HIV dynamic models incorporating drug adherence and resistance to treatment for prediction of virological responses," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3765-3778, March.
  12. Marc Lavielle & Adeline Samson & Ana Karina Fermin & France Mentré, 2011. "Maximum Likelihood Estimation of Long-Term HIV Dynamic Models and Antiviral Response," Biometrics, The International Biometric Society, vol. 67(1), pages 250-259, March.
  13. 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.
  14. 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.
  15. Lee, Kyoungjae & Lee, Jaeyong & Dass, Sarat C., 2018. "Inference for differential equation models using relaxation via dynamical systems," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 116-134.
  16. Giles Hooker, 2009. "Forcing Function Diagnostics for Nonlinear Dynamics," Biometrics, The International Biometric Society, vol. 65(3), pages 928-936, September.
  17. Yangxin Huang & Getachew Dagne, 2011. "A Bayesian Approach to Joint Mixed-Effects Models with a Skew-Normal Distribution and Measurement Errors in Covariates," Biometrics, The International Biometric Society, vol. 67(1), pages 260-269, March.
  18. José Luis Castro-Montero & Edwin Alblas & Arthur Dyevre & Nicolas Lampach, 2018. "The Court of Justice and treaty revision: A case of strategic leniency?," European Union Politics, , vol. 19(4), pages 570-596, December.
  19. Mélanie Prague & Daniel Commenges & Julia Drylewicz & Rodolphe Thiébaut, 2012. "Treatment Monitoring of HIV-Infected Patients based on Mechanistic Models," Biometrics, The International Biometric Society, vol. 68(3), pages 902-911, September.
  20. 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.
  21. Qianwen Tan & Subhashis Ghosal, 2021. "Bayesian Analysis of Mixed-effect Regression Models Driven by Ordinary Differential Equations," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 3-29, May.
  22. Vallès Codina, Oriol, 2023. "Business cycles, sectoral price stabilization, and climate change mitigation: A model of multi-sector growth in the tradition of the Bielefeld disequilibrium approach," Journal of Economic Behavior & Organization, Elsevier, vol. 216(C), pages 636-653.
  23. Huang Yangxin & Chen Ren & Dagne Getachew, 2011. "Simultaneous Bayesian Inference for Linear, Nonlinear and Semiparametric Mixed-Effects Models with Skew-Normality and Measurement Errors in Covariates," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-28, January.
  24. Commenges, D. & Jolly, D. & Drylewicz, J. & Putter, H. & Thiébaut, R., 2011. "Inference in HIV dynamics models via hierarchical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 446-456, January.
  25. David Lunn & Robert J B Goudie & Chen Wei & Oliver Kaltz & Olivier Restif, 2013. "Modelling the Dynamics of an Experimental Host-Pathogen Microcosm within a Hierarchical Bayesian Framework," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-15, August.
  26. Giles Hooker, 2010. "Comments on: Dynamic relations for sparsely sampled Gaussian processes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 19(1), pages 50-53, May.
  27. Qiu, Xing & Xu, Tao & Soltanalizadeh, Babak & Wu, Hulin, 2022. "Identifiability analysis of linear ordinary differential equation systems with a single trajectory," Applied Mathematics and Computation, Elsevier, vol. 430(C).
  28. 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).
  29. Yangxin Huang & Getachew Dagne, 2012. "Bayesian Semiparametric Nonlinear Mixed-Effects Joint Models for Data with Skewness, Missing Responses, and Measurement Errors in Covariates," Biometrics, The International Biometric Society, vol. 68(3), pages 943-953, September.
  30. Li, Ruqi & Song, Yurong & Wang, Haiyan & Jiang, Guo-Ping & Xiao, Min, 2023. "Reactive–diffusion epidemic model on human mobility networks: Analysis and applications to COVID-19 in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
  31. 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.
  32. Hanwen Huang & Andreas Handel & Xiao Song, 2020. "A Bayesian approach to estimate parameters of ordinary differential equation," Computational Statistics, Springer, vol. 35(3), pages 1481-1499, September.
  33. Yangxin Huang & X. Hu & Getachew Dagne, 2014. "Jointly modeling time-to-event and longitudinal data: a Bayesian approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 95-121, March.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.