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) : Martin, R D ; Yohai, Victor J


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a200632.pdf


Report Date : 04 Feb 1987


Pagination or Media Count : 20


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 (AR(1)) 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 AR(1) model, Fisher consistency being the first property one usually establishes along the way to proving consistency.


Descriptors :   *REGRESSION ANALYSIS , *KALMAN FILTERING , MULTIVARIATE ANALYSIS , REJECTION , MATHEMATICAL FILTERS , ASYMPTOTIC SERIES , PARAMETERS , MODELS , TRACKING


Subject Categories : Statistics and Probability


Distribution Statement : APPROVED FOR PUBLIC RELEASE