In this article, we introduce two new families of multivariate association measures based on power divergence and alpha divergence that recover both linear and nonlinear dependence relationships between multiple sets of random vectors. Importantly, this novel approach not only characterizes...

The treelet transform is a recent data reduction technique from the field of machine learning. Sharing many similarities with principal component analysis, the treelet transform can reduce a multidimensional dataset to the projections on a small number of directions or components that account...

The main result of this article states that one can get as many as D+1 modes from just a two component normal mixture in D dimensions. Multivariate mixture models are widely used for modeling homogeneous populations and for cluster analysis. Either the components directly or modes arising from...

Change point detection in sequences of functional data is examined where the functional observations are dependent. Of particular interest is the case where the change point is an epidemic change (a change occurs and then the observations return to baseline at a later time). The theoretical...

In hedonic regression models of the valuation of works of art, the age or period at which an artist produces a particular work is often found to have highly significant predictive value. Most existing results are based on regressions that pool many painters. Although the uniqueness of artists'...

A highly accurate demand forecast is fundamental to the success of every revenue management model. As is often required in both practice and theory, we aim to forecast the accumulated booking curve, as well as the number of reservations expected for each day in the booking horizon. To reduce the...

Sufficient dimension reduction techniques are to deal with curse of dimensionality when the underlying model is of a very general semiparametric multi-index structure and to estimate the central subspace spanned by the indices. However, the cost is that they can only identify the central...

A new dimension reduction method is proposed for functional multivariate regression with a multivariate response and a functional predictor by extending the functional sliced inverse regression model. Naive application of existing dimension reduction techniques for univariate response will...

Matrices of binary or count data are modelled under a unified statistical framework using finite mixtures to group the rows and/or columns. These likelihood-based one-mode and two-mode fuzzy clusterings provide maximum likelihood estimation of parameters and the options of using likelihood ratio...

Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, high-dimensional data are nowadays more and more frequent and, unfortunately, classical model-based clustering techniques show a disappointing behavior in high-dimensional...