In the present paper, we propose an exact test on the structure of the covariance matrix. In its development the properties of the Wishart distribution are used. Unlike the classical likelihood-ratio type tests and the tests based on the empirical distance, whose statistics depend on the total variance and the generalized variance only, the proposed approach provides more information about the ...

Multiscale analysis of univariate time series has appeared in the literature at an ever increasing rate. Here we introduce the multiscale analysis of covariance between two time series using the discrete wavelet transform. The wavelet covariance and wavelet correlation are deened and applied to this problem as an alternative to traditional cross-spectrum analysis. The wavelet covariance is show...

Forecasts of various quantities over multiple time periods and/or spatial expanses are required to operate modern power systems. Furthermore, probabilistic forecasts necessary facilitate economic decision-making and risk management. This gives rise the challenge producing which capture dependency between variables, time, locations. The Gaussian Copula has been widely used for multivariate energ...

Summary In this paper, some multivariate and double modelling approaches are presented. Moreover, article provides an overview of the structure covariance matrix. Furthermore, methods identification given.

For any given number of factors, Minimum Rank Factor Analysis yields optimal communalities for an observed covariance matrix in the sense that the unexplained common variance with that number of factors is minimized, subject to the constraint that both the diagonal matrix of unique variances and the observed covariance matrix minus that diagonal matrix are positive semidefinite. As a result, it...

Based on the Q-function, the conditional expectation of the logarithm of the jointlikelihood between responses and random effects, we propose a case-deletion approach to identify influential subjects and influential observations in linear mixed models. The models considered here are very broad in the sense that any covariance structures can be specified in the covariance matrices of the random ...

In this paper we present some new results concerning the classification in small sample high dimensional case. We discuss geometric properties of data structures in high dimensions. It is known that such a data form in high dimension an almost regular simplex even if co-variance structure of data is not unity. We restrict our attention to two class discrimination problems. It is assumed that ob...