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This paper discusses the constrained two stage least squares (CLS2) estimator of the parameters of ARCH models under known order. This estimator is a modified version of the two stage least squares (TSLS) estimation. The estimator is easy to obtain and fast since it involves only quadratic optimization. At the same time, the estimator has the same(More)
The case where the data sample size is finite and the least-squares-forward (LSF) method is used for autoregressive (AR) parameter estimation is considered. New formulas describing the residual variance and the prediction error behaviors in AR parameter estimation are derived, and the relation between the residual variance and the prediction error is(More)
  • 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, the position of an autonomous underwater vehicle (AUV) has been estimated by fusion of the data of two sensors: Doppler velocity log (DVL) and inertial navigation system (INS). Two different filters have been used in order to estimate the position of AUV, namely, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The approach of(More)
One of the approaches that can be used in autoregressive (AR) model order selection is to choose the order that minimizes the prediction error. The final prediction error (FPE) criterion uses this approach in order selection. Unfortunately, this criterion has poor performance in the finite sample case. In this paper, new theoretical approximations are(More)