Iteratively reweighted optimum linear regression in the presence of generalized Gaussian noise

Generalized Gaussian distribution (GGD) is one of the most prominent and widely used parametric distributions to model the statistical properties of various phenomena. In this paper, we consider the linear regression problem in the presence of GGD noise employing the iteratively reweighted least squ...

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Bibliographic Details
Main Authors: Fuxi Wen, Wei Liu
Format: Conference Proceeding
Language:English
Subjects:
Convergence
generalized Gaussian distribution
Iteratively reweighted least squares
Linear regression
Minimization
Shape
shape parameter
Signal processing algorithms
Signal to noise ratio
smoothed approximation
ℓ p -norm
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Summary:Generalized Gaussian distribution (GGD) is one of the most prominent and widely used parametric distributions to model the statistical properties of various phenomena. In this paper, we consider the linear regression problem in the presence of GGD noise employing the iteratively reweighted least squares (IRLS) algorithm. For the standard IRLS algorithm, an ℓ p -norm minimization problem is solved iteratively. However, its performance depends on a properly chosen norm parameter p. To solve this problem, we propose a modified IRLS algorithm with a variable p, which is noise distribution dependent and can be updated online. Numerical studies show that the proposed method can normally converge within a few iterations. Furthermore, optimal performance is achieved in terms of normalized mean square error for different GGD noise models.
ISSN:2165-3577
DOI:10.1109/ICDSP.2016.7868640