相关数据集
A change-point–based control chart for detecting sparse mean changes in high-dimensional heteroscedastic data
Because of the “curse of dimensionality,” high-dimensional processes present challenges to traditional multivariate statistical process monitoring (SPM) techniques. In addition, the unknown underlying
DataCite Commons2024-01-17 更新140
Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction
Sufficient dimension reduction (SDR) methods target finding lower-dimensional representations of a multivariate predictor to preserve all the information about the conditional distribution of the resp
DataCite Commons2024-02-15 更新100
Skeleton Clustering: Dimension-Free Density-Aided Clustering
We introduce a density-aided clustering method called Skeleton Clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensio
DataCite Commons2023-03-06 更新80
A Powerful Bayesian Test for Equality of Means in High Dimensions
We develop a Bayes factor based testing procedure for comparing two population means in high dimensional settings. In ‘large-p-small-n’ settings, Bayes factors based on proper priors require eliciting
DataCite Commons2020-09-01 更新140
RAPTT: An Exact Two-Sample Test in High Dimensions Using Random Projections
In high dimensions, the classical Hotelling’s T 2 test tends to have low power or becomes undefined due to singularity of the sample covariance matrix. In this article, this problem i
DataCite Commons2024-03-24 更新90
