Several matrix variate hypergeometric type distributions are derived. The compound distributions of left-spherical matrix variate elliptical distributions and inverted hypergeometric type distributions with matrix arguments are then proposed. The scale mixture of left-spherical matrix variate elliptical distributions and univariate inverted hypergeometric type distributions is also derived as a...

Semi-algebraic sets occur naturally when dealing with implicit models and boolean operations between them. In this work we present an algorithm to efficiently and in a certified way compute the connected components of semi-algebraic sets given by intersection or union of conjunctions of bi-variate equalities and inequalities. For any given precision, this algorithm can also provide a polygonal ...

We study the relationship between incidence for lung cancer in males in the Tuscan Region and material deprivation defined at census block level. We developed a bi-variate hierarchical Bayesian model to assess completeness of registration of incidence data and we proposed a series of random effect hierarchical Bayesian models to estimate the degree of association with material deprivation. Mode...

We propose a flexible regression model to study the association between a functional response and multiple functional covariates that are observed on the same domain. Specifically, we relate the mean of current response to current values of the covariates by sum of smooth unknown bi-variate functions, where each of the functions depends on the current value of the covariate and the time point i...

Different materials reflect light in different ways, so reflectance is a useful surface descriptor. Existing systems for measuring reflectance are cumbersome, however, and although the process can be streamlined using cameras, projectors and clever catadioptrics, it generally requires complex infrastructure. In this paper we propose a simpler method for inferring reflectance from images, one th...

We study training and generalization for multi-variate time series processing. It is suggested to used a quasi maximum likelihood approach rather than the standard sum of squared errors, thus taking dependencies among the errors of the individual time series into account. This may lead to improved generalization performance. Further, we extend the Optimal Brain Damage pruning technique to the m...

We study training and generalization for multi-variate time series processing. It is suggested to used a quasi maximum likelihood approach rather than the standard sum of squared errors, thus taking dependencies among the errors of the individual time series into account. This may lead to improved generalization performance. Further, we extend the Optimal Brain Damage pruning technique to the m...

Most conventional law equation discovery systems such as BACON require experimental environments to acquire their necessary data. The mathematical techniques such as linear system identi cation and neural network tting presume the classes of equations to model given observed data sets. The study reported in this paper proposes a novel method to discover an admissible model equation from a given...

We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ1 penalty to the means of the biclusters in order to obtain sparse and interpretable...

This article examines associations of socio-demographic and health-care indicators, and the statistic 'mortality amenable to health care' (amenable mortality) across the US states. There is over two-fold variation in amenable mortality, strongly associated with the percentages of state populations that are poor or black. Controlling for poverty and race with bi- and multi-variate analyses, seve...