搜索结果: 1-6 共查到“理论统计学 high-dimensional data”相关记录6条 . 查询时间(0.181 秒)
Sparse linear discriminant analysis by thresholding for high dimensional data
Classification high dimensionality misclassification rate nor-mality optimal classification rule sparse estimates
2011/6/20
In many social, economical, biological and medical studies, one
objective is to classify a subject into one of several classes based on
a set of variables observed from the subject. Because the prob...
Optimal properties of centroid-based classifiers for very high-dimensional data
Centroid method classification discrimination distance-basedclassifiers high-dimensional data location differences minimax performance
2010/3/11
We show that scale-adjusted versions of the centroid-based classi-
fier enjoys optimal properties when used to discriminate between two
very high-dimensional populations where the principal differen...
A two-sample test for high-dimensional data with applications to gene-set testing
High dimension gene-set testing large p small n martingale central limit theorem multiple comparison
2010/3/10
We propose a two-sample test for the means of high-dimensional
data when the data dimension is much larger than the sample size.
Hotelling’s classical T 2 test does not work for this “large p, small...
Asymptotic inference for high-dimensional data
Covariance matrix estimation c0 functional genomics highdimensionaldata infinite-dimensional central limit theorem
2010/3/10
In this paper, we study inference for high-dimensional data characterized
by small sample sizes relative to the dimension of the data.
In particular, we provide an infinite-dimensional framework to ...
Estimating Bayesian Networks for High-dimensional Data with Complex Mean Structure
Bayesian networks complex mean structure high-dimensionaldata regulatory networks
2010/3/10
The estimation of Bayesian networks given high-dimensional data sets,
in particular given gene expression data sets, has been the focus of much
recent research. While there are many methods availabl...
Robustness and accuracy of methods for high dimensional data analysis based on Student's t statistic
Bootstrap central limit theorem classication dimension reduction higher criticism large deviation probability
2010/3/9
Student's t statistic is nding applications today that were never envisaged
when it was introduced more than a century ago. Many of these applications
rely on properties, for example robustness aga...