期刊文献+

基于扰动模型的非线性ODE局部影响分析

Local influence analysis of nolinear ordinary differential equations based on perturbation model
下载PDF
导出
摘要 通过引入B样条基函数,给出非线性常微分方程中未知参数的两步估计法.基于扰动模型以及广义影响函数的概念,进行局部影响分析问题的讨论,给出基于扰动模型的广义Cook距离的计算公式.结果显示该算法可降低计算量,提高计算效率. One technique for estimating parameters of nonlinear ordinary differential equations and detecting the influential observations when the parameters being estimated was presented by introducing B-spline basis func-tions and perturbation model respectively .Generalized influential function is mentioned in the local influential a-nalysis .In the end ,the formula of generalized Cook′s distance is given ,from which we can find that the algo-rithm will reduce computational load and improve the efficiency of computing .
出处 《纺织高校基础科学学报》 CAS 2014年第1期113-115,共3页 Basic Sciences Journal of Textile Universities
基金 青年科学基金资助项目(11201360)
关键词 非线性常微分方程 B样条基函数 广义影响函数 扰动模型 nonlinear ordinary differential equations B-spline basis functions generalized influential function perturbation model
  • 相关文献

参考文献6

  • 1RAMSAY J O, HOOKER G,CAMPBELL D. Parameter estimation for differential equations: A generalized smoothing approach[J]. Journal of Royal Statistical Society, 2007,69 (5) : 741-796.
  • 2CAO Jiguo,RAMSAY J O. Parameter cascades and profiling infunctional data analysis[J]. Computational Statistics, 2007,22(3) :335-351.
  • 3ZHOU Jie, HAN Lu, LIU Sanyang. Nonlinear mixed effects state space models with applications to HIV dynamics[J]. Statistics and Probability Letters, 2013,83 (5) : 1448-1456.
  • 4SHI Lei. Local influence in principal components analysis[J]. Biometrika Trust, 1997,84(1): 175-186.
  • 5SHI Lei, WANG Xueren. Local influence in ridge regression[J ]. Statistics and Data Analysis, 1999,31 (3) : 341-353.
  • 6周杰,刘三阳,周芳,WU HuLin.HIV模型的统计诊断[J].科学通报,2012,57(8):666-672. 被引量:3

二级参考文献13

  • 1Ho D D,Neumann A U,Perelson A S,et al.Rapid turnover of plasma virions and CD4lymphocytes in HIV-1infection.Nature,1995,373:123–126.
  • 2Perelson A S.Mathematical analysis of HIV-1dynamics in vivo.SIAM Rev,1999,41:3–44.
  • 3Bates D M,Watts D G.Nonlinear Regression and Its Application.New York:John Wiley&Sons,Inc,1988.
  • 4Ramsay J O,Hook G,Campbell,et al.Parameter estimation for differential equation:A generalized smoothing approach.J Roy Stat Soc B,2007,69:741–796.
  • 5Cao J,Ramsay J O.Parameter cascades and profiling in functional data analysis.Comput Stat,2007,22:335–351.
  • 6Poyton A A,Varziri M S,McAuley K B,et al.Parameter estimation in continuous time dynamic models using principal differential anal-ysis.Comput Chem Eng,2006,30:698–708.
  • 7Liang H,Wu H L.Parameter estimation for differential equation models using a framework of measurement error in regression models.J Am Stat Assoc,2008,103:1570–1583.
  • 8Chen J,Wu H L.Efficient local estimation for time varying coefficients in deterministic dynamic models with applications to HIV-1dy-namics.J Am Stat Assoc,2008,103:369–384.
  • 9Chen J,Wu H L.Estimation of time varying parameters in deterministic dynamic models.Statist Sin,2008,18:987–1006.
  • 10Liang H,Miao H Y,Wu H L.Estimation of constant and time-varying dynamic parameters of HIV infection in a nonlinear differential equation model.Ann Appl Stat,2010,4:460–483.

共引文献2

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部