Mostafa Karimi

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  • M. Karimi
  • 2007
An estimate for the prediction error of the least-squares-forward (LSF) autoregressive (AR) parameter estimation method has been recently proposed. In this paper, this estimate is used for deriving a new AR model order selection criterion. This new criterion is an estimate of the Kullback-Leibler index and can replace the Akaike information criterion (AIC)(More)
— In this paper, we present a compressive sampling and Multi-Hypothesis (MH) reconstruction strategy for video sequences which has a rather simple encoder, while the decoding system is not that complex. We introduce a convex cost function that incorporates the MH technique with the sparsity constraint and the Tikhonov regularization. Consequently, we derive(More)
The Least-Squares-Forward-Backward (LSFB) method for estimating the parameters of the autoregressive (AR) model is considered and new theoretical approximations for expectations of the prediction error and the residual variance are derived. These results are used for modifying the AR order selection criteria FPE and AIC. The performance of these modified(More)
In this paper the student-t autoregressive conditional heteroscedasticity (ARCH) model is considered and a new estimator for the coefficients of the ARCH model and the degree of freedom of the Student-t noise is proposed. ARCH models have been used in numerous applications to model the unpredictability and the strong dependence of the instantaneous(More)
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