In this paper we propose a robust probabilistic multivariate calibration (RPMC) model in an attempt to identify linear relationships between two sets of observed variables contaminated with outliers. Instead of the Gaussian assumptions that predominate in classical statistical models, RPMC is closely related with the multivariate Student's t-distribution over noises and latent variables. As a r...

Abstract We study multivariate polynomials over ‘structured’ grids. Firstly, we propose an interpretation as to what it means for a finite subset of field be structured; do so by numerical parameter, the nullity. then extend several results – notably, Combinatorial Nullstellensatz and Coefficient Theorem structured The main point is that structure grid allows degree constraints on relaxed.

The framework of this article is modelling applied to sheries. It aims at relating some classical stock-recruitment curves to a dynamic structured model, consisting of the adults stages of the stock and a pre-recruits stage (i.e. eggs, larvae, juveniles). The main result is that no functional stock-recruitment relationship can be found, unless further restrictive assumptions are added to the mo...

We have proposed the deep-structured conditional random fields (CRFs) for sequential labeling and classification recently. The core of this model is its deep structure and its discriminative nature. This paper outlines the learning strategies and algorithms we have developed for the deep-structured CRFs, with a focus on the new strategy that combines the layer-wise unsupervised pre-training usi...

Empirical research on European stock markets has shown that they behave differently according to the performance of the leading financial market identified as the US market. A positive sign is viewed as good news in the international financial markets, a negative sign means, conversely, bad news. As a result, we assume that European stock market returns are affected by endogenous and exogenous ...

This paper generalizes the model of Becker, Grossman, and Murphy (1994) to the multivariate case. The multivariate model generates Frisch demand functions where current consumption is related to prices of all goods, and lagged and future consumption of all goods. The theoretical restrictions are that current price effects (holding lagged and future consumption constant) are negative definite, a...

We address the task of annotating images with semantic tuples. Solving this problem requires an algorithm able to deal with hundreds of classes for each argument of the tuple. In such contexts, data sparsity becomes a key challenge. We propose handling this sparsity by incorporating feature representations of both the inputs (images) and outputs (argument classes) into a factorized log-linear m...

We present a new multivariate technique for testing the significance of individual terms in a multifactorial analysis-of-variance model for multispecies response variables. The technique will allow researchers to base analyses on measures of association (distance measures) that are ecologically relevant. In addition, unlike other distance-based hypothesis-testing techniques, this method allows ...

We conducted a multicentre cohort study of 228 patients with osteoarthritis followed up after total hip or knee replacement. Quality of life and patient satisfaction were assessed by self-administered questionnaires. Patient satisfaction was the dependent variable in a multivariate linear regression model. Independent variables included sociodemographic factors, pre- and post-operative clinical...

Often when modeling structured domains, it is desirable to leverage information that is not naturally expressed as simply a label. Examples include knowledge about the evaluation measure that will be used at test time, and partial (weak) label information. When the additional information has structure that factorizes according to small subsets of variables (i.e., is low order, or decomposable),...