Information Covariance Matrices for Multivariate Burr III and Logistic Distributions
Authors
Abstract:
Main result of this paper is to derive the exact analytical expressions of information and covariance matrices for multivariate Burr III and logistic distributions. These distributions arise as tractable parametric models in price and income distributions, reliability, economics, Human population, some biological organisms to model agricultural population data and survival data. We showed that all the calculations can be obtained from one main moment multi dimensional integral whose expression is obtained through some particular change of variables. Indeed, we consider that this calculus technique for improper integral has its own importance .
similar resources
Information and Covariance Matrices for Multivariate Pareto (IV), Burr, and Related Distributions
Main result of this paper is to derive the exact analytical expressions of information and covariance matrix for multivariate Pareto, Burr and related distributions. These distributions arise as tractable parametric models in reliability, actuarial science, economics, finance and telecommunications. We showed that all the calculations can be obtained from one main moment multidimensional integr...
full textVisualizing Distributions of Covariance Matrices
We present some methods for graphing distributions of covariance matrices and demonstrate them on several models, including the Wishart, inverse-Wishart, and scaled inverse-Wishart families in different dimensions. Our visualizations follow the principle of decomposing a covariance matrix into scale parameters and correlations, pulling out marginal summaries where possible and using two and thr...
full textComputing Posterior Distributions for Covariance Matrices
Diiculties in computing the posterior distribution of a covariance matrix when using nonconjugate priors has been discussed by several authors. Typically, the posterior distribution for the covariance matrix is computed via the Gibbs sampler and when using a Wishart prior for the inverse of the covariance matrix, one obtains conditional conjugacy (the full conditional distribution of the invers...
full textStructured covariance matrices in multivariate regression models
A similarity matrix is a covariance matrix generated by additive nested common factors having independent components. The set of such matrices is a structured subset of covariance matrices, closed under permutation and restriction, which makes it potentially useful as a sub-model for the joint dependence of several responses. It is also equal to the set of rooted trees. Some issues connected wi...
full textPoint and Interval Estimation for the Burr Type III Distribution
In this paper, we study the estimation problems for the Burr type III distribution based on a complete sample. The maximum likelihood method is used to derive the point estimators of the parameter. An exact confidence interval and an exact joint confidence region for the parameters are constructed. Two numerical examples with real data set and simulated data, are presented to illustrate the met...
full textShrinkage estimators for large covariance matrices in multivariate real and complex normal distributions under an invariant quadratic loss
The problem of estimating large covariance matrices of multivariate real normal and complex normal distributions is considered when the dimension of the variables is larger than the number of sample size. The Stein-Haff identities and calculus on eigenstructures for singular Wishart matrices are developed for real and complex cases, respectively. By using these techniques, the unbiased risk est...
full textMy Resources
Journal title
volume 19 issue 6
pages 9- 20
publication date 2008-08
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023