4 citations · 7 across the 7 of their papers we have counts for
7 papers
Test for mean matrix in GMANOVA model under heteroscedasticity and non-normality for high-dimensional data
Takayuki Yamada, Tetsuto Himeno, Annika Tillander +1
This paper is concerned with the testing bilateral linear hypothesis on the mean matrix in the context of the generalized multivariate analysis of variance (GMANOVA) model when the…
Sequential sampling of junction trees for decomposable graphs
Jimmy Olsson, Tetyana Pavlenko, Felix L. Rios
The junction-tree representation provides an attractive structural property for organizing a decomposable graph. In this study, we present two novel stochastic algorithms, which we…
Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs
Tatjana Pavlenko, Felix Leopoldo Rios
In this study, we present a multi-class graphical Bayesian predictive classifier that incorporates the uncertainty in the model selection into the standard Bayesian formalism. For…
A -classifier for high-dimensional data under non-normality
M. Rauf Ahmad, Tatjana Pavlenko
A classifier for two or more samples is proposed when the data are high-dimensional and the underlying distributions may be non-normal. The classifier is constructed as a linear co…
Goodness-of-fit tests based on sup-functionals of weighted empirical processes
Natalia Stepanova, Tatjana Pavlenko
A large class of goodness-of-fit test statistics based on sup-functionals of weighted empirical processes is proposed and studied. The weight functions employed are Erdős-Feller-Ko…
Asymptotic Properties of the Misclassification Errors for Euclidean Distance Discriminant Rule in High-Dimensional Data
H. Watanabe, M. Hyodo, T. Seo +1
Performance accuracy of the Euclidean Distance Discriminant rule (EDDR) is studied in the high-dimensional asymptotic framework which allows the dimensionality to exceed sample siz…
Bayesian Network Classifiers in a High Dimensional Framework
Tatjana Pavlenko, Dietrich von Rosen
We present a growing dimension asymptotic formalism. The perspective in this paper is classification theory and we show that it can accommodate probabilistic networks classifiers,…