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A method for proving the asymptotic normality of linear rank statistics is presented. Use is made of the fact that the variances of many rank statistics met in practice admit a factorization expressed in terms of the sample size. The method of proof consists of two steps corresponding to the limiting behavior of the modulus and argument of the characteristic function of the rank statistic.

A recently proposed method of multiple frequency estimation for mixed-spectrum time series is analyzed. The so-called PF method is a procedure that combines the autoregressive (AR) representation of superimposed sinusoids with the idea of parametric filtering. The gist of the method is to parametrize a linear filter in accord with a certain parametrization property, as suggested by the particular form of the bias encountered by Prony's least-squares estimator for the AR model. It is shown that for any parametric filter with this property, the least-squares estimator obtained from the filtered data is almost surely contractive as a function of the filter parameter and has a unique multivariate fixed-point in the vicinity of the true AR parameter. The fixed-point, known as the PF estimator, is shown to be stronly consistent for estimating the AR model, and the chronic bias of Prony's estimator is thus eliminated. The almost sure convergence of an iterative algorithm that calculates the fixed-point and the asymptotic normality of the PF estimator are also established. The all-pole filter is considered as an example and application of the developed theory.Copyright 1993, 1999 Academic Press