1 edition of Robust and nonlinear time series analysis found in the catalog.
|Statement||edited by J. Franke, W. Härdle, and D. Martin.|
|Series||Lecture notes in statistics -- 26, Lecture notes in statistics (Springer-Verlag) -- v. 26.|
|Contributions||Franke, J., Härdle, W., Martin, D., Sonderforschungsbereich 123--"Stochastische Mathematische Modelle.".|
|The Physical Object|
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Robust multivariate and nonlinear time series models: Application of robust estimators for the vector autoregressive and bilinear time series models by Ravi Ramakrishnan (Author). : Understanding Robust and Exploratory Data Analysis (): Hoaglin, David C., Mosteller, Frederick, Tukey, John W.: Books5/5(1).
Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. Rating: (not yet rated) 0 with reviews - Be the first. Pris: kr.
Häftad, Skickas inom vardagar. Köp Robust and Nonlinear Time Series Analysis av J Franke, Wolfgang Hardle, D Martin på Robust Multivariate and Nonlinear Time Series Models. There are at least two reasons why robust regression techniques are useful tools in robust time series analysis.
First of Author: Ravi Ramakrishnan. A robust estimation procedure for multiple time series is proposed based on robustifying the residual autocovariances in the estimating equation.
Robust Regression by Means of Sestimators in Robust and Nonlinear Time Series Analysis:edited by (). In this article, we study parametric robust estimation in nonlinear regression models with regressors generated by a class of non-stationary and null recurrent Markov process.
In this paper, methods of nonlinear time series analysis, including time delay embedding, nonlinear deterministic prediction and Wayland test were applied to Author: Bruce Mizrach.