A HIGH DIMENSIONAL TWO SAMPLE SIGNIFICANCE TEST

By Arthur P. Dempster. 1958

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

EFFECT OF HIGH DIMENSION: BY AN EXAMPLE OF A TWO SAMPLE PROBLEM (1999, methodology): “Dempster (1958, 1960) proposed a non-exact test for the hypothesis described in Section 2, with the dimension of data possibly greater than the sample degrees of freedom.”
ON ESTIMATION OF THE POPULATION SPECTRAL DISTRIBUTION FROM A HIGH‐DIMENSIONAL SAMPLE COVARIANCE MATRIX (2010, methodology): “C© 2010 Australian Statistical Publishing Association Inc. Published by Blackwell Publishing Asia Pty Ltd. Australian & New Zealand Journal of Statistics As a historical example, Dempster (1958, 1960) proposed a so-called non-exact test to correct the deficiency of the Hotelling T 2-test.”
Two-sample tests for high-dimension, strongly spiked eigenvalue models (2016, background): “In such situations, Dempster (1958, 1960) and Srivastava (2007) considered the test when π 1 and π 2 are Gaussian.”
A More Powerful Two-Sample Test in High Dimensions using Random Projection (2011, methodology): “As such, it is reasonable to interpret this ratio as a measure of (3)Two other high-dimensional tests have been proposed in older works [6, 19, 20] that lead to the asymptotic power function βCQ, but under more restrictive assumptions.”
Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size (2002, methodology): “The Annals of Statistics 2002, Vol. 30, No. 4, 1081–1102”
Likelihood Ratio Tests for High‐Dimensional Normal Distributions (2013, methodology): “The failure of the traditional multivariate method for high-dimensional data had been observed by Dempster [13] in as early as 1958.”
Large Sample Covariance Matrices and High-Dimensional Data Analysis (2015, background): “Nonparametric Estimation under Shape Constraints, by Piet Groeneboom and Geurt Jungbloed www.cambridge.org© in this web service Cambridge University Press Cambridge University Press 978-1-107-06517-8 - Large Sample Covariance Matrices and High-Dimensional Data Analysis Jianfeng Yao, Shurong Zheng an”
Testing for complete independence in high dimensions (2005, background): “Some examples of work on inference problems in this high-dimensional setting can be found in Bai & Saranadasa (1996), Dempster (1958, 1960), Ledoit & Wolf (2002), Saranadasa (1993) and Schott (2006).”
Multivariate-Sign-Based High-Dimensional Tests for the Two-Sample Location Problem (2016, methodology): “This challenge calls for new statistical tests to deal with high-dimensional data, see Dempster (1958), Bai and Saranadasa (1996), Srivastava andDu (2008), andChen andQin (2010) for two-sample tests for means, Ledoit and Wolf (2002), Schott (2005), Chen, Zhang, and Zhong (2010), and Zou et al.…”
Multivariate Theory for Analyzing High Dimensional Data (2007, methodology): “Dempster (1958), nevertheless proposed the same test FD when Σ = σ2I, since it can be computed for all values of p irrespective of whether n ≤ p or n > p.”
A test for the mean vector with fewer observations than the dimension (2008, methodology): “Other tests available in the literature for N p include the tests proposed by Dempster [4,5] and Bai and Saranadasa [3], respectively.”
A Simple Two-Sample Test in High Dimensions Based on L2-Norm (2020, background): “Note that study of the high-dimensional two-sample problem can be dated back to Dempster (1958, 1960).”
Asymptotic Results of a High Dimensional MANOVA Test and Power Comparison When the Dimension is Large Compared to the Sample Size (2004, methodology): “For the null distribution of TD ,i t has been proposed to use F -approximations by Dempster (1958, 1960) , Takeda and Goto (1999), etc.”