Least-Squares Independence Test
Summary :
Identifying the statistical independence of random variables is one of the important tasks in statistical data analysis. In this paper, we propose a novel non-parametric independence test based on a least-squares density ratio estimator. Our method, called least-squares independence test (LSIT), is distribution-free, and thus it is more flexible than parametric approaches. Furthermore, it is equipped with a model selection procedure based on cross-validation. This is a significant advantage over existing non-parametric approaches which often require manual parameter tuning. The usefulness of the proposed method is shown through numerical experiments.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.6 pp.1333-1336
- Publication Date
- 2011/06/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1333
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Keyword
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Masashi SUGIYAMA, Taiji SUZUKI, "Least-Squares Independence Test" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 6, pp. 1333-1336, June 2011, doi: 10.1587/transinf.E94.D.1333.
Abstract: Identifying the statistical independence of random variables is one of the important tasks in statistical data analysis. In this paper, we propose a novel non-parametric independence test based on a least-squares density ratio estimator. Our method, called least-squares independence test (LSIT), is distribution-free, and thus it is more flexible than parametric approaches. Furthermore, it is equipped with a model selection procedure based on cross-validation. This is a significant advantage over existing non-parametric approaches which often require manual parameter tuning. The usefulness of the proposed method is shown through numerical experiments.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1333/_p
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@ARTICLE{e94-d_6_1333,
author={Masashi SUGIYAMA, Taiji SUZUKI, },
journal={IEICE TRANSACTIONS on Information},
title={Least-Squares Independence Test},
year={2011},
volume={E94-D},
number={6},
pages={1333-1336},
abstract={Identifying the statistical independence of random variables is one of the important tasks in statistical data analysis. In this paper, we propose a novel non-parametric independence test based on a least-squares density ratio estimator. Our method, called least-squares independence test (LSIT), is distribution-free, and thus it is more flexible than parametric approaches. Furthermore, it is equipped with a model selection procedure based on cross-validation. This is a significant advantage over existing non-parametric approaches which often require manual parameter tuning. The usefulness of the proposed method is shown through numerical experiments.},
keywords={},
doi={10.1587/transinf.E94.D.1333},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Least-Squares Independence Test
T2 - IEICE TRANSACTIONS on Information
SP - 1333
EP - 1336
AU - Masashi SUGIYAMA
AU - Taiji SUZUKI
PY - 2011
DO - 10.1587/transinf.E94.D.1333
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2011
AB - Identifying the statistical independence of random variables is one of the important tasks in statistical data analysis. In this paper, we propose a novel non-parametric independence test based on a least-squares density ratio estimator. Our method, called least-squares independence test (LSIT), is distribution-free, and thus it is more flexible than parametric approaches. Furthermore, it is equipped with a model selection procedure based on cross-validation. This is a significant advantage over existing non-parametric approaches which often require manual parameter tuning. The usefulness of the proposed method is shown through numerical experiments.
ER -
English
