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|Title:||Filtering of the ARMAX process with generalized t -distribution noise: The influence function approach|
|Authors:||Ho, W.K. |
|Source:||Ho, W.K., Ling, K.V., Vu, H.D., Wang, X. (2014-04-30). Filtering of the ARMAX process with generalized t -distribution noise: The influence function approach. Industrial and Engineering Chemistry Research 53 (17) : 7019-7028. ScholarBank@NUS Repository. https://doi.org/10.1021/ie401990x|
|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. © 2014 American Chemical Society.|
|Source Title:||Industrial and Engineering Chemistry Research|
|Appears in Collections:||Staff Publications|
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