Sea Spectral Estimation Using ARMA Models

Robust Estimation for Arma Models

This paper introduces a new class of robust estimates for ARMA models. They are M-estimates, but the residuals are computed so the effect of one outlier is limited to the period where it occurs. These estimates are closely related to those based on a robust filter, but they have two important advantages: they are consistent and the asymptotic theory is tractable. We perform a Monte Carlo where ...

Improved ARMA spectral estimation using the canonical variate method

A superresolution method of ARMA spectral estimation

Recently, a method for generating an ARMA spectral estimator model which possessed super— resolution performance was developed [2]. This method entailed minimizing a weighted quadratic functional of a set of 'basic error terms." An issue which remained to be resolved at that time was the selection of the weighting matrix that characterized the functional being minimized. A weighting matrix sele...

R-estimation for Arma Models

This paper is devoted to the R-estimation problem for the parameter of a stationary ARMA model. The asymptotic uniform linearity of a suitable vector of rank statistics leads to the asymptotic normality of √ n-consistent R-estimates resulting from the minimization of the norm of this vector. By using a discretized √ n-consistent preliminary estimate, we construct a new class of one-step R-estim...

Estimation in ARMA models based on signed ranks

In this paper we develop an asymptotic theory for estimation based on signed ranks in the ARMA model when the innovation density is symmetrical. We provide two classes of estimators and we establish their asymptotic normality with the help of the asymptotic properties for serial signed rank statistics. Finally, we compare our procedure to the one of least-squares, and we illustrate the performa...