The problem of estimation has been widely investigated with all different kinds of assumptions. This study focusses on the subjective Bayesian estimation of a location vector and characteristic matrix for the univariate and multivariate elliptical model as oppose to objective Bayesian estimation that has been thoroughly discussed (see Fang and Li (1999) amongst others). The prior distributions that will be assumed is the conjugate normal-inverse Wishart prior and also the normal-Wishart prior which has not yet been considered in literature. The posterior distributions, joint and marginal, as well as the Bayes estimators will be derived. The newly developed results are applied to the multivariate normal and multivariate t-distribution. For subjective Bayesian analysis the vector-spherical matrix elliptical model is also studied. / Dissertation (MSc)--University of Pretoria, 2012. / Statistics / MSc / Unrestricted

The primary objective of this dissertation is to extend the classical Principal Components Analysis (PCA), aiming to reduce the dimensionality of a large number of Normal interrelated variables, in two directions. The first is to go beyond the static (contemporaneous or synchronous) covariance matrix among these interrelated variables to include certain forms of temporal (over time) dependence. The second direction takes the form of extending the PCA model beyond the Normal multivariate distribution to the Elliptically Symmetric family of distributions, which includes the Normal, the Student's t, the Laplace and the Pearson type II distributions as special cases. The result of these extensions is called the Generalized principal component analysis (GPCA). The GPCA is illustrated using both Monte Carlo simulations as well as an empirical study, in an attempt to demonstrate the enhanced reliability of these more general factor models in the context of out-of-sample forecasting. The empirical study examines the predictive capacity of the GPCA method in the context of Exchange Rate Forecasting, showing how the GPCA method dominates forecasts based on existing standard methods, including the random walk models, with or without including macroeconomic fundamentals. / Ph. D.

Matrix variate skew elliptically contoured distributions generalize several classes of important distributions. This paper defines and explores matrix variate skew elliptically contoured distributions. In particular, we discuss two stochastic representations of the matrix variate skew elliptically contoured distributions.

Matrix variate skew elliptically contoured distributions generalize several classes of important distributions. This paper defines and explores matrix variate skew elliptically contoured distributions. In particular, we discuss the first two moments of the matrix variate skew elliptically contoured distributions.