Output distributional influence function

Author

Peltonen, Sari ; Kuosmanen, Pauli ; Astola, Jaakko

Author_Institution

Inst. of Signal Process., Tampere Univ. of Technol., Finland

Volume

49

Issue

9

fYear

2001

fDate

9/1/2001 12:00:00 AM

Firstpage

1953

Lastpage

1960

Abstract

When a filter is being selected for an application, it is often essential to know that the behavior of the filter does not change significantly if there are small deviations from the initial assumptions. This robustness of a filter is traditionally explored by means of the influence function (IF) and change-of-variance function (CVF). However, as these are asymptotic measures, there is uncertainty of the applicability of the obtained results to the finite-length filters used in the real-world filtering applications. We present a new method called the output distributional influence function (ODIF) that examines the robustness of the finite-length filters. The method gives most extensive information about the robustness for filters with a known output distribution function. As examples, the ODIFs for the distribution function, density function, expectation, and variance are given for the well-known mean and median filters and are interpreted in detail

Keywords

filtering theory; median filters; statistical analysis; asymptotic measures; change-of-variance function; density function; expectation; filter behavior; finite-length filters; mean filters; median filters; output distributional influence function; real-world filtering applications; variance; Books; Contamination; Density functional theory; Distribution functions; Filters; H infinity control; Measurement uncertainty; Robustness; Signal processing; Statistics;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.942624

Filename

942624