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Modified Gaussian likelihood estimators for ARMA models on d . (English) Zbl 1178.62096
Summary: For observations from an autoregressive moving-average process of any dimension, we propose a modification of the Gaussian likelihood, which when maximized corrects the edge-effects and fixes the order of the bias for the estimators derived. We show that the new estimators are not only consistent but also asymptotically normal for any dimension. A classical one-dimensional time series result for the variance matrix is established for any dimension that guarantees the efficiency of the estimators, if the original process is Gaussian. We followed a model-based approach and used finite numbers for the corrections per dimension, which are especially made for the case of autoregressive moving-average models of fixed order.
62M10Time series, auto-correlation, regression, etc. (statistics)
62F12Asymptotic properties of parametric estimators
62M09Non-Markovian processes: estimation
62H12Multivariate estimation
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