Least-squares two-sample test
type: Item
instance of: scholarly article
title: Least-squares two-sample test
zbMATH Open document ID: 1414.62311
author: Masashi Sugiyama
author: Taiji Suzuki
author: Yuta Itoh
author: Takafumi Kanamori
author: Manabu Kimura
published in: Neural Networks
publication date: 2013-05-31T00:00:00Z
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62D05
zbMATH DE Number: 6169050
zbMATH Keywords: two-sample test
zbMATH Keywords: homogeneity test
zbMATH Keywords: density ratio estimation
zbMATH Keywords: unconstrained least-squares importance fitting
zbMATH Keywords: Pearson divergence
Wikidata QID: Q51568967
describes a project that uses: ElemStatLearn
describes a project that uses: bootstrap
MaRDI profile type: Publication
full work available at URL: j.neunet.2011.04.003
OpenAlex ID: W2006501157
cites work: Q5601960
cites work: Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates
cites work: Theory of Reproducing Kernels
cites work: Q5515694
cites work: On the Asymptotic Properties of a Nonparametric&lt;tex&gt;$L_1$&lt;/tex&gt;-Test Statistic of Homogeneity
cites work: A Distribution Free Version of the Smirnov Two Sample Test in the $p$-Variate Case
cites work: Optimal rates for the regularized least-squares algorithm
cites work: Semiparametric density estimation under a two-sample density ratio model
cites work: Q5539510
cites work: Estimation of the information by an adaptive partitioning of the observation space
cites work: Q4318617
cites work: Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests
cites work: Learning Theory and Kernel Machines
cites work: Adaptive importance sampling for value function approximation in off-policy reinforcement learning
cites work: Permutation tests for equality of distributions in high-dimensional settings
cites work: The elements of statistical learning. Data mining, inference, and prediction
cites work: Q5807246
cites work: A least-squares approach to direct importance estimation
cites work: Computational complexity of kernel-based density-ratio estimation: a condition number analysis
cites work: Test of homogeneity in semiparametric two-sample density ratio models
cites work: Q5529067
cites work: On Information and Sufficiency
cites work: Nonparametric testing of closeness between two unknown distribution functions
cites work: 10.1162/153244302760200687
cites work: Integral Probability Metrics and Their Generating Classes of Functions
cites work: Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization
cites work: Inferences for case-control and semiparametric two-sample density ratio models
cites work: Soft margins for AdaBoost
cites work: On the influence of the kernel on the consistency of support vector machines
cites work: THE PROBABLE ERROR OF A MEAN
cites work: Dimensionality reduction for density ratio estimation in high-dimensional spaces
cites work: Q3174108
cites work: Direct importance estimation for covariate shift adaptation
cites work: Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search
cites work: Divergence Estimation of Continuous Distributions Based on Data-Dependent Partitions
DOI: 10.1016/J.NEUNET.2011.04.003
DBLP publication ID: journals/nn/SugiyamaSIKK11
description: scientific article; zbMATH DE number 6169050