AKAIKE INFORMATION CRITERION FOR SELECTING COMPONENTS OF THE MEAN VECTOR IN HIGH DIMENSIONAL DATA WITH FEWER OBSERVATIONS
スポンサーリンク
概要
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
- CLASSIFICATION WITH A PREASSIGNED ERROR RATE WHEN TWO COVARIANCE MATRICES ARE EQUAL (Statistical Region Estimation and Its Application)
- 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