Time-varying coefficient estimation in differential equation models with noisy time-varying covariates
We study the problem of estimating time-varying coefficients in ordinary differential equations. Current theory only applies to the case when the associated state variables are observed without measurement errors as presented in Chen and Wu (2008) [4] and [5]. The difficulty arises from the quadrati...
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sg-ntu-dr.10356-978282020-03-07T12:37:18Z Time-varying coefficient estimation in differential equation models with noisy time-varying covariates Hong, Zhaoping Lian, Heng School of Physical and Mathematical Sciences DRNTU::Science::Mathematics::Calculus We study the problem of estimating time-varying coefficients in ordinary differential equations. Current theory only applies to the case when the associated state variables are observed without measurement errors as presented in Chen and Wu (2008) [4] and [5]. The difficulty arises from the quadratic functional of observations that one needs to deal with instead of the linear functional that appears when state variables contain no measurement errors. We derive the asymptotic bias and variance for the previously proposed two-step estimators using quadratic regression functional theory. 2013-10-31T03:52:39Z 2019-12-06T19:47:09Z 2013-10-31T03:52:39Z 2019-12-06T19:47:09Z 2012 2012 Journal Article Hong, Z., & Lian, H. (2012). Time-varying coefficient estimation in differential equation models with noisy time-varying covariates. Journal of multivariate analysis, 103(1), 58-67. 0047-259X https://hdl.handle.net/10356/97828 http://hdl.handle.net/10220/17116 10.1016/j.jmva.2011.06.007 en Journal of multivariate analysis |
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DRNTU::Science::Mathematics::Calculus Hong, Zhaoping Lian, Heng Time-varying coefficient estimation in differential equation models with noisy time-varying covariates |
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We study the problem of estimating time-varying coefficients in ordinary differential equations. Current theory only applies to the case when the associated state variables are observed without measurement errors as presented in Chen and Wu (2008) [4] and [5]. The difficulty arises from the quadratic functional of observations that one needs to deal with instead of the linear functional that appears when state variables contain no measurement errors. We derive the asymptotic bias and variance for the previously proposed two-step estimators using quadratic regression functional theory. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Hong, Zhaoping Lian, Heng |
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Article |
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Hong, Zhaoping Lian, Heng |
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Hong, Zhaoping |
title |
Time-varying coefficient estimation in differential equation models with noisy time-varying covariates |
title_short |
Time-varying coefficient estimation in differential equation models with noisy time-varying covariates |
title_full |
Time-varying coefficient estimation in differential equation models with noisy time-varying covariates |
title_fullStr |
Time-varying coefficient estimation in differential equation models with noisy time-varying covariates |
title_full_unstemmed |
Time-varying coefficient estimation in differential equation models with noisy time-varying covariates |
title_sort |
time-varying coefficient estimation in differential equation models with noisy time-varying covariates |
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2013 |
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https://hdl.handle.net/10356/97828 http://hdl.handle.net/10220/17116 |
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