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A generalization of Wishart density for the case when the inverse of the covariance matrix is a band matrix

Kryštof Eben (1994)

Mathematica Bohemica

In a multivariate normal distribution, let the inverse of the covariance matrix be a band matrix. The distribution of the sufficient statistic for the covariance matrix is derived for this case. It is a generalization of the Wishart distribution. The distribution may be used for unbiased density estimation and construction of classification rules.

A generalized bivariate lifetime distribution based on parallel-series structures

Vahideh Mohtashami-Borzadaran, Mohammad Amini, Jafar Ahmadi (2019)

Kybernetika

In this paper, a generalized bivariate lifetime distribution is introduced. This new model is constructed based on a dependent model consisting of two parallel-series systems which have a random number of parallel subsystems with fixed components connected in series. The probability that one system fails before the other one is measured by using competing risks. Using the extreme-value copulas, the dependence structure of the proposed model is studied. Kendall's tau, Spearman's rho and tail dependences...

A geometric approach to correlation inequalities in the plane

A. Figalli, F. Maggi, A. Pratelli (2014)

Annales de l'I.H.P. Probabilités et statistiques

By elementary geometric arguments, correlation inequalities for radially symmetric probability measures are proved in the plane. Precisely, it is shown that the correlation ratio for pairs of width-decreasing sets is minimized within the class of infinite strips. Since open convex sets which are symmetric with respect to the origin turn out to be width-decreasing sets, Pitt’s Gaussian correlation inequality (the two-dimensional case of the long-standing Gaussian correlation conjecture) is derived...

A Global Approach to the Comparison of Clustering Results

Osvaldo Silva, Helena Bacelar-Nicolau, Fernando C. Nicolau (2012)

Biometrical Letters

The discovery of knowledge in the case of Hierarchical Cluster Analysis (HCA) depends on many factors, such as the clustering algorithms applied and the strategies developed in the initial stage of Cluster Analysis. We present a global approach for evaluating the quality of clustering results and making a comparison among different clustering algorithms using the relevant information available (e.g. the stability, isolation and homogeneity of the clusters). In addition, we present a visual method...

A Gram-Schmidt orthogonalizing process of design matrices in linear models as an estimating procedure of covariance components.

Gabriela Beganu (2005)

RACSAM

Se considera un modelo lineal mixto multivariante equilibrado sin interacción para el que las matrices de las formas cuadráticas necesarias para estimar la covarianza de las componentes se expresan mediante operadores lineales en espacios con producto interior de dimensión finita. El propósito de este artículo es demostrar que las formas cuadráticas obtenidas por el proceso de ortogonalización de Gram-Schmidt de las matrices de diseño son combinaciones lineales de las formas cuadráticas derivadas...

A graph-based estimator of the number of clusters

Gérard Biau, Benoît Cadre, Bruno Pelletier (2007)

ESAIM: Probability and Statistics

Assessing the number of clusters of a statistical population is one of the essential issues of unsupervised learning. Given n independent observations X1,...,Xn drawn from an unknown multivariate probability density f, we propose a new approach to estimate the number of connected components, or clusters, of the t-level set ( t ) = { x : f ( x ) t } . The basic idea is to form a rough skeleton of the set ( t ) using any preliminary estimator of f, and to count the number of connected components of the resulting graph. Under...

A homogeneity test of large dimensional covariance matrices under non-normality

M. Rauf Ahmad (2018)

Kybernetika

A test statistic for homogeneity of two or more covariance matrices is presented when the distributions may be non-normal and the dimension may exceed the sample size. Using the Frobenius norm of the difference of null and alternative hypotheses, the statistic is constructed as a linear combination of consistent, location-invariant, estimators of trace functions that constitute the norm. These estimators are defined as U -statistics and the corresponding theory is exploited to derive the normal limit...

A learning algorithm combining functional discriminant coordinates and functional principal components

Tomasz Górecki, Mirosław Krzyśko (2014)

Discussiones Mathematicae Probability and Statistics

A new type of discriminant space for functional data is presented, combining the advantages of a functional discriminant coordinate space and a functional principal component space. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 35 functional data sets (time series). Experiments show that constructed combined space provides a higher quality of classification of LDA method compared with component spaces.

A method for knowledge integration

Martin Janžura, Pavel Boček (1998)

Kybernetika

With the aid of Markov Chain Monte Carlo methods we can sample even from complex multi-dimensional distributions which cannot be exactly calculated. Thus, an application to the problem of knowledge integration (e. g. in expert systems) is straightforward.

A multi-agent brokerage platform for media content recommendation

Bruno Veloso, Benedita Malheiro, Juan Carlos Burguillo (2015)

International Journal of Applied Mathematics and Computer Science

Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer...

A new family of trivariate proper quasi-copulas

Manuel Úbeda-Flores (2007)

Kybernetika

In this paper, we provide a new family of trivariate proper quasi-copulas. As an application, we show that W 3 – the best-possible lower bound for the set of trivariate quasi-copulas (and copulas) – is the limit member of this family, showing how the mass of W 3 is distributed on the plane x + y + z = 2 of [ 0 , 1 ] 3 in an easy manner, and providing the generalization of this result to n dimensions.

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