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Least-squares two-sample test.

Masashi Sugiyama1, Taiji Suzuki, Yuta Itoh

  • 1Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan. sugi@cs.titech.ac.jp

Neural Networks : the Official Journal of the International Neural Network Society
|May 17, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new non-parametric two-sample test using a least-squares density ratio estimator. The novel method reduces the probability of missing differences between distributions compared to existing techniques.

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • The two-sample test, also known as the homogeneity test, is crucial for comparing probability distributions.
  • Existing methods may have limitations in accurately detecting differences between distributions.

Purpose of the Study:

  • To propose a novel non-parametric method for the two-sample test.
  • To evaluate the performance of the proposed method against state-of-the-art techniques.

Main Methods:

  • Development of a non-parametric two-sample test.
  • Utilizing a least-squares density ratio estimator.
  • Experimental validation of the proposed method.

Main Results:

  • The proposed method demonstrates a lower type-II error rate compared to a state-of-the-art method.
  • A slight increase in type-I error was observed.
  • The method effectively identifies differences between probability distributions.

Conclusions:

  • The novel least-squares density ratio estimator offers a promising approach for two-sample testing.
  • The trade-off between type-I and type-II errors suggests potential applications where sensitivity is prioritized.