COMPARISON OF DISCRIMINATION METHODS FOR HIGH DIMENSIONAL DATA

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概要

• 論文の詳細を見る

• 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 two groups, when p is large, is considered. Three procedures based on the Moore-Penrose inverse of the sample covariance matrix, and an empirical Bayes estimate of the precision matrix are proposed and compared with the DLDA procedure.

• 一般社団法人日本統計学会の論文

著者

• Srivastava Muni

  Department Of Statistics University Of Toronto

• KUBOKAWA Tatsuya

  Faculty of Economics, University of Tokyo

• Kubokawa Tatsuya

  Faculty Of Economics University Of Tokyo

関連論文

• Variable Selection in Multivariate Linear Regression Models with Fewer Observations than the Dimension
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• PROFILE ANALYSIS FOR A GROWTH CURVE MODEL
• AN EMPIRICAL BAYES INFORMATION CRITERION FOR SELECTING VARIABLES IN LINEAR MIXED MODELS
• AKAIKE INFORMATION CRITERION FOR SELECTING COMPONENTS OF THE MEAN VECTOR IN HIGH DIMENSIONAL DATA WITH FEWER OBSERVATIONS
• COMPARISON OF DISCRIMINATION METHODS FOR HIGH DIMENSIONAL DATA
• PREDICTION IN MULTIVARIATE MIXED LINEAR MODELS
• ESTIMATION OF BOUNDED LOCATION AND SCALE PARAMETERS
• MINIMAXITY IN ESTIMATION OF RESTRICTED PARAMETERS
• ESTIMATION OF VARIANCE AND COVARIANCE COMPONENTS IN ELLIPTICALLY CONTOURED DISTRIBUTIONS
• CHARACTERIZATION OF PRIORS IN THE STEIN PROBLEM
• INTEGRAL INEQUALITY FOR MINIMAXITY IN THE STEIN PROBLEM
• SOME TESTS CONCERNING THE COVARIANCE MATRIX IN HIGH DIMENSIONAL DATA
• Estimation of EPMC for High-dimensional Data(Session 3b)
• PROFILE ANALYSIS WITH RANDOM-EFFECTS COVARIANCE STRUCTURE

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