Robust time series analysis: a survey
Norbert Stockinger, Rudolf Dutter (1987)
Kybernetika
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Displaying similar documents to “AR models with uniformly distributed noise”
Robust time series analysis: a survey
Robustness of estimation of first-order autoregressive model under contaminated uniform white noise
Karima Nouali (2009)
Discussiones Mathematicae Probability and Statistics
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The first-order autoregressive model with uniform innovations is considered. In this paper, we study the bias-robustness and MSE-robustness of modified maximum likelihood estimator of parameter of the model against departures from distribution of white noise. We used the generalized Beta distribution to describe these departures.
On unequally spaced AR(1) process
Jan Šindelář, Jiří Knížek (2003)
Kybernetika
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Discrete autoregressive process of the first order is considered. The process is observed at unequally spaced time instants. Both least squares estimate and maximum likelihood estimate of the autocorrelation coefficient are analyzed. We show some dangers related with the estimates when the true value of the autocorrelation coefficient is small. Monte-Carlo method is used to illustrate the problems.
On variance-covariance components estimation in linear models with AR(1) disturbances.
Witkovský, V. (1996)
Acta Mathematica Universitatis Comenianae. New Series
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On the efficiency of procedures for estimation of parameters in ARIMA models.
Bala Chandra (1984)
Trabajos de Estadística e Investigación Operativa
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The paper discusses the implementation of the Newton-Raphson iterative method of estimation of parameters in the autoregressive integrated moving average (ARIMA) models. The efficiency of this method has been compared with other well known methods of estimation.
Robust estimation and forecasting for beta-mixed hierarchical models of grouped binary data.
Maxim A. Pashkevich, Yurij S. Kharin (2004)
SORT
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The paper focuses on robust estimation and forecasting techniques for grouped binary data with misclassified responses. It is assumed that the data are described by the beta-mixed hierarchical model (the beta-binomial or the beta-logistic), while the misclassifications are caused by the stochastic additive distorsions of binary observations. For these models, the effect of ignoring the misclassifications is evaluated and expressions for the biases of the method-of-moments estimators...
On geometric ergodicity and prediction in nonnegative non-linear autoregressive processes
Petr Zvára (2004)
Kybernetika
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A non-linear AR(1) process is investigated when the associated white noise is positive. A criterion is derived for the geometric ergodicity of the process. Some explicit formulas are derived for one and two steps ahead extrapolation. Influence of parameter estimation on extrapolation is studied.