Regression Analysis with Less Information Loss under the Existence of Background Noise
Summary :
As is well-known, the ordinary regression analysis method is confined to a simplified linear model of the estimation based on the Gaussian property and a least squares error criterion. Then, usually the prediction is done through the transformation based on this regression function. In this paper, a new trial for the regression analysis is proposed especially in the form matched to the complexity of physical phenomena and stochastic signal detection under the existence of background noise. Furthermore, the prediction of the output probability distribution is done based on the regression relationship with less information loss. Finally, the effectiveness of the proposed method is confirmed experimentally by applying it to the actual acoustic data.
- Publication
- IEICE TRANSACTIONS on transactions Vol.E72-E No.5 pp.531-538
- Publication Date
- 1989/05/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Information Theory and Its Applications)
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Mitsuo OHTA, Bing CHANG, Yegui XIAO, "Regression Analysis with Less Information Loss under the Existence of Background Noise" in IEICE TRANSACTIONS on transactions,
vol. E72-E, no. 5, pp. 531-538, May 1989, doi: .
Abstract: As is well-known, the ordinary regression analysis method is confined to a simplified linear model of the estimation based on the Gaussian property and a least squares error criterion. Then, usually the prediction is done through the transformation based on this regression function. In this paper, a new trial for the regression analysis is proposed especially in the form matched to the complexity of physical phenomena and stochastic signal detection under the existence of background noise. Furthermore, the prediction of the output probability distribution is done based on the regression relationship with less information loss. Finally, the effectiveness of the proposed method is confirmed experimentally by applying it to the actual acoustic data.
URL: https://global.ieice.org/en_transactions/transactions/10.1587/e72-e_5_531/_p
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@ARTICLE{e72-e_5_531,
author={Mitsuo OHTA, Bing CHANG, Yegui XIAO, },
journal={IEICE TRANSACTIONS on transactions},
title={Regression Analysis with Less Information Loss under the Existence of Background Noise},
year={1989},
volume={E72-E},
number={5},
pages={531-538},
abstract={As is well-known, the ordinary regression analysis method is confined to a simplified linear model of the estimation based on the Gaussian property and a least squares error criterion. Then, usually the prediction is done through the transformation based on this regression function. In this paper, a new trial for the regression analysis is proposed especially in the form matched to the complexity of physical phenomena and stochastic signal detection under the existence of background noise. Furthermore, the prediction of the output probability distribution is done based on the regression relationship with less information loss. Finally, the effectiveness of the proposed method is confirmed experimentally by applying it to the actual acoustic data.},
keywords={},
doi={},
ISSN={},
month={May},}
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TY - JOUR
TI - Regression Analysis with Less Information Loss under the Existence of Background Noise
T2 - IEICE TRANSACTIONS on transactions
SP - 531
EP - 538
AU - Mitsuo OHTA
AU - Bing CHANG
AU - Yegui XIAO
PY - 1989
DO -
JO - IEICE TRANSACTIONS on transactions
SN -
VL - E72-E
IS - 5
JA - IEICE TRANSACTIONS on transactions
Y1 - May 1989
AB - As is well-known, the ordinary regression analysis method is confined to a simplified linear model of the estimation based on the Gaussian property and a least squares error criterion. Then, usually the prediction is done through the transformation based on this regression function. In this paper, a new trial for the regression analysis is proposed especially in the form matched to the complexity of physical phenomena and stochastic signal detection under the existence of background noise. Furthermore, the prediction of the output probability distribution is done based on the regression relationship with less information loss. Finally, the effectiveness of the proposed method is confirmed experimentally by applying it to the actual acoustic data.
ER -
English
