Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions.

Unlabelled: Mixtures of multivariate leptokurtic-normal distributions have been recently introduced in the clustering literature based on mixtures of elliptical heavy-tailed distributions. They have the advantage of having parameters directly related to the moments of practical interest. We derive two estimation procedures for these mixtures.

Sucrose Phosphate Synthase A2 Affects Carbon Partitioning and Drought Response.

Sucrose is essential for plants for several reasons: It is a source of energy, a signaling molecule, and a source of carbon skeletons. Sucrose phosphate synthase (SPS) catalyzes the conversion of uridine diphosphate glucose and fructose-6-phosphate to sucrose-6-phosphate, which is rapidly dephosphorylated by sucrose phosphatase. SPS is critical in the accumulation of sucrose because it catalyzes an irreversible reaction.

Multiple scaled symmetric distributions in allometric studies.

In allometric studies, the joint distribution of the log-transformed morphometric variables is typically symmetric and with heavy tails. Moreover, in the bivariate case, it is customary to explain the morphometric variation of these variables by fitting a convenient line, as for example the first principal component (PC). To account for all these peculiarities, we propose the use of multiple scaled symmetric (MSS) distributions.

Allometric analysis using the multivariate shifted exponential normal distribution.

In allometric studies, the joint distribution of the log-transformed morphometric variables is typically elliptical and with heavy tails. To account for these peculiarities, we introduce the multivariate shifted exponential normal (MSEN) distribution , an elliptical heavy-tailed generalization of the multivariate normal (MN). The MSEN belongs to the family of MN scale mixtures (MNSMs) by choosing a convenient shifted exponential as mixing distribution.

The Kullback-Leibler autodependogram.

The autodependogram is a graphical device recently proposed in the literature to analyze autodependencies. It is defined computing the classical Pearson -statistics of independence at various lags in order to point out the presence lag-depedencies. This paper proposes an improvement of this diagram obtained by substituting the -statistics with an estimator of the Kullback-Leibler divergence between the bivariate density of two delayed variables and the product of their marginal distributions.