Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103383
Title: Filtering of the ARMAX process with generalized t -distribution noise : the influence function approach
Authors: Ho, Weng Khuen
Ling, Keck Voon
Vu, Hoang Dung
Wang, Xiaoqiong
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2014
Source: Ho, W. K., Ling, K. V., Vu, H. D., & Wang, X. (2014). Filtering of the ARMAX Process with Generalized t -Distribution Noise: The Influence Function Approach . Industrial & Engineering Chemistry Research, 53(17), 7019-7028.
Series/Report no.: Industrial & engineering chemistry research
Abstract: The commonly made assumption of Gaussian noise is an approximation to reality. In this paper, influence function, an analysis tool in robust statistics, is used to formulate a recursive solution for the filtering of the ARMAX process with generalized t-distribution noise. By being a superset encompassing Gaussian, uniform, t, and double exponential distributions, generalized t-distribution has the flexibility of characterizing noise with Gaussian or non-Gaussian statistical properties. The filter is formulated as a maximum likelihood problem, but instead of solving the optimization problem numerically, influence function approximation is used to obtain a recursive solution to reduce the computational load and facilitate real-time implementation. The influence function equations derived are also useful in determining the variance of the filter and the impact of outliers.
URI: https://hdl.handle.net/10356/103383
http://hdl.handle.net/10220/19958
DOI: 10.1021/ie401990x
Rights: © 2014 American Chemical Society. This is the author created version of a work that has been peer reviewed and accepted for publication by Industrial & Engineering Chemistry Research, American Chemical Society. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [DOI: http://dx.doi.org/10.1021/ie401990x].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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