期刊文献+

对非线性常微分方程的参数估计和局部影响分析

Parameter Estimation and Local Influence Analysis of Nolinear Ordinary Differential Equations Based on Case-deletion Model
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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。然后导出基于数据删除模型的广义Cook距离的计算公式,并说明该算法降低了计算量。最终利用FitzHugh-Nagumo方程的模拟实验,检验了该算法的有效性,同时与现有算法进行比较,分析证明了其优越性。 This paper presents a technique for estimating parameters of nonlinear ordinary differential equations by using B-spline Basis Functions. Case-deletion model is introduced to give formula of Generalized Cook's Distance. This algorithm can reduce computational load and improve the efficiency of computing. Effectiveness of the algorithm is checked by FitzHugh-Nagumo equation, and the superiority of the algorithm is demonstrated by a comparison with the algorithms available.
出处 《电子科技》 2014年第1期4-6,共3页 Electronic Science and Technology
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 数据删除模型 广义COOK距离 nonlinear ordinary differential equations b-spline basis function case-deletion model cook's distance
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参考文献7

  • 1RAMSAY J O,HOOKER G,CAMPBELL D. Parameter estimation for differential equations:a generalized smoothing approach[J].Journal of Royal Statistical Society,2007,(02):741-796.
  • 2CAO JIGUO,JAMES O RAMSAY. Parameter cascades and profiling infunctional data analysis[J].{H}COMPUTATIONAL STATISTICS,2007,(22):335-351.
  • 3ZHOU Jie,HAN Lu,LIU Sanyang. Nonlinear mixed effects state space models with applications to HIV dynamics[J].{H}Statistics & Probability Letters,2013,(05):1448-1456.
  • 4韦博成;林金官;解锋昌.统计诊断[M]{H}北京:高等教育出版社,2009.
  • 5SHI Lei. Local influence in principal components analysis[J].Biometrika Trust,1997,(06):175-186.
  • 6SHI Lei,WANG Xueren. Local influence in ridge regression.computational[J].Statistics and Data Analysis,1999,(02):341-353.
  • 7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.

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