A statistical study of a regularized method for long auto-regressive spectral estimation

The authors address the problem of power spectral density estimation of time series with auto-regressive (AR) models when only a short span of data is available for analysis. The AR coefficients are estimated through a regularized method proposed by G. Kitagawa and W. Gersch (1985). An experimental study of this method and a comparison with the classical least squares (LS) method are outlined. The principles of the statistical study and computation results are presented.<>