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Truncated sequential estimation of the parameter of a first order autoregressive process with dependent noises
Authors:D Fourdrinier  V Konev  S Pergamenshchikov
Institution:(1) LITIS EA 4108, Univ. Rouen, Rouen, France;(2) Dept. Appl. Math. and Cybernetics, Tomsk State University, Tomsk, Russia;(3) Laboratoire de Math. R. Salem, Univ. Rouen, Rouen, France
Abstract:For a first-order non-explosive autoregressive process with dependent noise, we propose a truncated sequential procedure with a fixed mean-square accuracy. The asymptotic distribution of the estimator depends on the type of the noise distribution: it is normal when the noise has a Kotz’s distribution, while it is a mixture of normal distributions if the noise distribution is a variance mixture of normal distrbutions as well. In both cases, the convergence to the limiting distribution is uniform in the unknown parameter.
Keywords:autoregression model  truncated sequential estimators  uniform normality
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