scholarly journals Application of the empirical Bayes approach to nonparametric testing for high-dimensional data

2010 ◽  
Vol 51 ◽  
Author(s):  
Gintautas Jakimauskas ◽  
Jurgis Sušinskas
Keyword(s):  
Empirical Bayes ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Sequential Data ◽  
Data Partition ◽  
Test Statistics ◽  
Computationally Efficient ◽  
Small Power ◽  
Nonparametric Testing ◽  
Empirical Bayes Estimators

In [5] a simple, data-driven and computationally efficient procedure of (nonparametric) testing for high-dimensional data have been introduced. The procedure is based on randomization and resampling, a special sequential data partition procedure, and χ2-type test statistics. However, the χ2 test has small power when deviations from the null hypothesis are small or sparse. In this note test statistics based on the nonparametric maximum likelihood and the empirical Bayes estimators.

On Test Statistics in Profile Analysis with High-dimensional Data

2014 ◽  
Vol 45 (10) ◽  
pp. 3716-3743 ◽  
Author(s):  
Mizuki Onozawa ◽  
Takahiro Nishiyama ◽  
Takashi Seo
Keyword(s):  
Profile Analysis ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Test Statistics

scholarly journals Efficient algorithm for testing goodness-of-fit for classification of high dimensional data

2009 ◽  
Vol 50 ◽  
Author(s):  
Gintautas Jakimauskas
Keyword(s):  
Goodness Of Fit ◽  
Projection Pursuit ◽  
Classification Problem ◽  
Gaussian Mixture ◽  
Sequential Data ◽  
Data Partition ◽  
Computationally Efficient ◽  
Efficient Procedure ◽  
Selection Of

Let us have a sample satisfying d-dimensional Gaussian mixture model (d is supposed to be large). The problem of classification of the sample is considered. Because of large dimension it is natural to project the sample to k-dimensional (k = 1,  2, . . .) linear subspaces using projection pursuit method which gives the best selection of these subspaces. Having an estimate of the discriminant subspace we can perform classification using projected sample thus avoiding ’curse of dimensionality’.  An essential step in this method is testing goodness-of-fit of the estimated d-dimensional model assuming that distribution on the complement space is standard Gaussian. We present a simple, data-driven and computationally efficient procedure for testing goodness-of-fit. The procedure is based on well-known interpretation of testing goodness-of-fit as the classification problem, a special sequential data partition procedure, randomization and resampling, elements of sequentialtesting.Monte-Carlosimulations are used to assess the performance of the procedure.

scholarly journals Nonparametric Tests Applicable to High Dimensional Data

2019 ◽  
Vol 48 (4) ◽  
pp. 14-42
Author(s):  
Frantisek Rublik
Keyword(s):  
Sample Size ◽  
Location Problem ◽  
High Dimensional Data ◽  
Multiple Comparisons ◽  
Nonparametric Tests ◽  
Data Driven ◽  
High Dimensional ◽  
Test Statistics ◽  
Dissimilarity Measures

Constructions of data driven ordering of set of multivariate observations are presented. The methods employ also dissimilarity measures. The ranks are used in the construction of test statistics for location problem and in the construction of the corresponding multiple comparisons rule. An important aspect of the resulting procedures is that they can be used also in the multisample setting and in situations where the sample size is smaller than the dimension of the observations. The performance of the proposed procedures is illustrated by simulations.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional
Sign in / Sign up

Export Citation Format

Share Document