journalLONG - TIME ERROR ESTIMATION ANDA STABILITY - SMOOTHING
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چکیده
منابع مشابه
Consistency & Numerical Smoothing ⇒ Error Estimation — An Alternative of the Lax - Richtmyer Theorem
We all know Lax-Richtmyer Theorem: Assuming consistency, convergence is equivalent to numerical stability. However, it is in practice very difficult to verify the numerical stability of a scheme while solving an evolution equation, especially if the equation is nonlinear and/or the scheme is complex. Consequently, a large gap exists between error analysis theory and numerical computation practi...
متن کاملLong - Time Error Estimation and a Stability Indicator
Long-time error estimates are abstractly given for a large class of initial value problems without using the traditional concept of \numerical stability". Instead of numerical error propagation, we consider exact error propagation by splitting the error of a numerical initial value problem into local error and propagated global error in a way diierent from the traditional one. The advantage is ...
متن کاملTwo-step Smoothing Estimation of the Time-variant Parameter with Application to Temperature Data
‎In this article‎, ‎we develop two nonparametric smoothing estimators for parameter of a time-variant parametric model‎. ‎This parameter can be from any parametric family or from any parametric or semi-parametric regression model‎. ‎Estimation is based on a two-step procedure‎, ‎in which we first get the raw estimate of the parameter at a set of disjoint time...
متن کاملData Assimilation via Error Subspace Statistical Estimation. Part I: Theory and Schemes
A rational approach is used to identify efficient schemes for data assimilation in nonlinear ocean–atmosphere models. The conditional mean, a minimum of several cost functionals, is chosen for an optimal estimate. After stating the present goals and describing some of the existing schemes, the constraints and issues particular to ocean–atmosphere data assimilation are emphasized. An approximati...
متن کاملConvergence Rates for Smoothing Spline Estimators in Varying Coefficient Models
We consider the estimation of a multiple regression model in which the coefficients change slowly in “time”, with “time” being an additional covariate. Under reasonable smoothness conditions, we prove the usual expected mean square error bounds for the smoothing spline estimators of the coefficient functions.
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