Within the framework of Algorithmic Inference, we recall a linear regression analysis tool based on the identification of the joint probability distribution of the regression coefficients compatible with the sampled data and aimed at finding out the independent components of this distribution. On this distribution we implement specific Independent Component Analysis (ICA) procedures to obtain the parameter independent components giving rise to suitable confidence regions also when the noise term is far from being independent and identically Gaussian.
Regressing data with independent parameters / B. Apolloni, S. Bassis - In: Neural Nets WIRN10 : proceedings of the 20th italian workshop on neural nets, may 27-29 2010, Vietri sul Mare, Salerno, Italy / [a cura di] C. F. Morabito, B. Apolloni, S. Bassis. - [s.l] : IOS Press, 2011 Feb. - ISBN 978160750-6911. - pp. 34-43 (( Intervento presentato al 20th. convegno Italian Workshop on Neural Nets tenutosi a Vietri sul Mare, Italy nel 2010.
Regressing data with independent parameters
B. ApolloniPrimo
;S. BassisUltimo
2011
Abstract
Within the framework of Algorithmic Inference, we recall a linear regression analysis tool based on the identification of the joint probability distribution of the regression coefficients compatible with the sampled data and aimed at finding out the independent components of this distribution. On this distribution we implement specific Independent Component Analysis (ICA) procedures to obtain the parameter independent components giving rise to suitable confidence regions also when the noise term is far from being independent and identically Gaussian.
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