Article-Journal

Distance-weighted discrimination of face images for gender classification
We illustrate the advantages of distance weighted discrimination for classification and feature extraction in a High Dimension Low …
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Efficient ANOVA for directional data
In this paper, we tackle the ANOVA problem for directional data. We apply the invariance principle to construct locally and …
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Functional principal component regression: An overview and a comparative study
Functional data analysis is a field of growing importance in Statistics. In particular, the functional linear model with scalar …
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Goodness-of-fit test for randomly censored data based on maximum correlation
In this paper we study a goodness-of-fit test based on the maximum correlation coefficient, in the context of randomly censored data. …
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Inference on the mode of weak directional signals: A Le Cam perspective on hypothesis testing near singularities
We revisit, in an original and challenging perspective, the problem of testing the null hypothesis that the mode of a directional …
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On the efficiency of some rank-based test for the homogeneity of concentrations
Recently, Verdebout (2015) introduced a Kruskal-Wallis type rank-based procedure $\phi^{(n)}_{V}$ to test the homogeneity of …
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On the estimation of the density of a directional data stream
Many directional data such as wind directions can be collected extremely easily so that experiments typically yield a huge number of …
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Preliminary test estimation for multi-sample principal components
Point estimation is considered in a multi-sample principal components setup, in a situation where it is suspected that the hypothesis …
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Resampling based inference for a distribution function using censored ranked set samples
This article deals with constructing confidence intervals/bands for a distribution function based on censored ranked set samples. …
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Skew-rotationally-symmetric distributions and related efficient inferential procedures
Most commonly used distributions on the unit hypersphere $\mathcal{S}^{k-1}=\{\mathbf{v} \in \mathbb{R}^k: \mathbf{v}^{\top} …
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