Accession Number:

ADA200632

Title:

Fisher Consistency of AM-Estimates of the Autoregression Parameter Using Hard Rejection Filter Cleaners

Descriptive Note:

Technical rept. no. 96, 1 Dec 1986-30 Nov 1987

Corporate Author:

WASHINGTON UNIV SEATTLE DEPT OF STATISTICS

Personal Author(s):

Report Date:

1987-02-04

Pagination or Media Count:

20.0

Abstract:

In recent years several classes of robust estimates of ARMA model parameters have been proposed. The AM-estimates seem most appealing They are based in on an intuitively appealing robust filter cleaner which cleans the data by replacing outliners with interpolates based on previous cleaned data. They have proved quite useful in a variety of applications. On the other hand, the AM-estimates are sufficiently complicated functions of the data that it has proven difficult to establish even the most basic asymptotic properties such as consistency. This paper considers only a special case of AM-estimates based on a so-called hard-rejection filter cleaner. The importance of hard-rejection filter-cleaners, which are described for the first-order autoregressive AR1 model, is that engineers often use a similar intuitively appealing modification of the Kalman filter for dealing with outliers in tracking problems. Under certain assumptions these special AM-estimates are Fisher consistent for the parameter Phi sub 0 of an AR1 model, Fisher consistency being the first property one usually establishes along the way to proving consistency.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE