Displaying similar documents to “Robust time series analysis: a survey”

An alternative analysis of variance.

Nicholas T. Longford (2008)

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The one-way analysis of variance is a staple of elementary statistics courses. The hypothesis test of homogeneity of the means encourages the use of the selected-model based estimators which are usually assessed without any regard for the uncertainty about the outcome of the test. We expose the weaknesses of such estimators when the uncertainty is taken into account, as it should be, and propose synthetic estimators as an alternative.

On robust GMM estimation with applications in economics and finance

Ansgar Steland (2000)

Discussiones Mathematicae Probability and Statistics

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Generalized Methods of Moments (GMM) estimators are a popular tool in econometrics since introduced by Hansen (1982), because this approach provides feasible solutions for many problems present in economic data where least squares or maximum likelihood methods fail when naively applied. These problems may arise in errors-in-variable regression, estimation of labor demand curves, and asset pricing in finance, which are discussed here. In this paper we study a GMM estimator for the rank...

On-line nonparametric estimation.

Rafail Khasminskii (2004)

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A survey of some recent results on nonparametric on-line estimation is presented. The first result deals with an on-line estimation for a smooth signal S(t) in the classic 'signal plus Gaussian white noise' model. Then an analogous on-line estimator for the regression estimation problem with equidistant design is described and justified. Finally some preliminary results related to the on-line estimation for the diffusion observed process are described.

Bayes unbiased estimators of parameters of linear trend with autoregressive errors

František Štulajter (1987)

Aplikace matematiky

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The method of least wquares is usually used in a linear regression model 𝐘 = 𝐗 β + ϵ for estimating unknown parameters β . The case when ϵ is an autoregressive process of the first order and the matrix 𝐗 corresponds to a linear trend is studied and the Bayes approach is used for estimating the parameters β . Unbiased Bayes estimators are derived for the case of a small number of observations. These estimators are compared with the locally best unbiased ones and with the usual least squares estimators. ...