Robust time series analysis: a survey
Norbert Stockinger, Rudolf Dutter (1987)
Kybernetika
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Displaying similar documents to “Generalized Levinson-Durbin sequences and binomial coefficients.”
Robust time series analysis: a survey
Estimation of summary characteristics from replicated spatial point processes
Zbyněk Pawlas (2011)
Kybernetika
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Summary characteristics play an important role in the analysis of spatial point processes. We discuss various approaches to estimating summary characteristics from replicated observations of a stationary point process. The estimators are compared with respect to their integrated squared error. Simulations for three basic types of point processes help to indicate the best way of pooling the subwindow estimators. The most appropriate way depends on the particular summary characteristic,...
Classification of linear estimators
Pavel Kovanic (1979)
Kybernetika
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Tables for processes with exponential white noise
Jiří Anděl, Karel Zvára (1988)
Kybernetika
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Mean squared errors of prediction by kriging in linear models with errors.
Štulajter, F. (1994)
Acta Mathematica Universitatis Comenianae. New Series
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On the estimation of the autocorrelation function
Manuel Duarte Ortigueira (2010)
Discussiones Mathematicae Probability and Statistics
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The autocorrelation function has a very important role in several application areas involving stochastic processes. In fact, it assumes the theoretical base for Spectral analysis, ARMA (and generalizations) modeling, detection, etc. However and as it is well known, the results obtained with the more current estimates of the autocorrelation function (biased or not) are frequently bad, even when we have access to a large number of points. On the other hand, in some applications, we need...
Spatial prediction of the mark of a location-dependent marked point process: How the use of a parametric model may improve prediction
Tomáš Mrkvička, François Goreaud, Joël Chadoeuf (2011)
Kybernetika
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We discuss the prediction of a spatial variable of a multivariate mark composed of both dependent and explanatory variables. The marks are location-dependent and they are attached to a point process. We assume that the marks are assigned independently, conditionally on an unknown underlying parametric field. We compare (i) the classical non-parametric Nadaraya-Watson kernel estimator based on the dependent variable (ii) estimators obtained under an assumption of local parametric model...