搜索结果: 1-12 共查到“管理学 high-dimensional data”相关记录12条 . 查询时间(0.783 秒)
Identification of Signal, Noise, and Indistinguishable Subsets in High-Dimensional Data Analysis
Two-Level Thresholding Signal detection False positive control False negative control Multiple testing Variable screening
2013/6/13
Motivated by applications in high-dimensional data analysis where strong signals often stand out easily and weak ones may be indistinguishable from the noise, we develop a statistical framework to pro...
Modelling interactions in high-dimensional data with Backtracking
Backtracking interactions Lasso parallel computing path algorithm.
2012/9/17
We study the problem of high-dimensional regression when there may be interacting vari-ables. We introduce a new idea called Backtracking, that can be incorporated into many existing high-dimensional ...
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...
Some Tests Concerning the Covariance Matrix in High Dimensional Data
asymptotic distributions multivariate normal null and non-null distributions sample size smaller than the dimension
2009/3/9
In this paper, tests are developed for testing certain hypotheses on the covariance matrix Σ, when the sample size N = n + 1 is smaller than the dimension pof the data. Under the condition that (tr Σi...
Comparison of Discrimination Methods for High Dimensional Data
classification discrimination analysis minimum distance Moore-Penrose inverse
2009/3/5
In microarray experiments, the dimension p of the data is very large but there are only a few observations N on the subjects/patients. In this article, the problem of classifying a subject into one of...
Multivariate Theory for Analyzing High Dimensional Data
distribution of test statistics DNA microarray data fewer observations than dimension multivariate analysis of variance singular Wishart
2009/3/5
In this article, we develop a multivariate theory for analyzing multivariate datasets that have fewer observations than dimensions. More specifically, we consider the problem of testing the hypothesis...
Akaike Information Criterion for Selecting Components of the Mean Vector in High Dimensional Data with Fewer Observations
Akaike information criterion high correlation high dimensional model ridge estimator selection of means
2009/3/5
The Akaike information criterion (AIC) has been successfully used in the literature in model selection when there are a small number of parameters p and a large number of observations N. The cases whe...