On the conditional probability density functions of multivariate uniform random vectors and multivariate normal random vectors

Random Vectors from the Multivariate

The multivariate normal distribution is often the assumed distribution underlying data samples and it is widely used in pattern recognition and classiication 2]]3]]6]]7]. It is undoubtedly of great beneet to be able to generate random values and vectors from the distribution of choice given its suucient statistics or chosen parameters. We present a detailed account of the theory and algorithms ...

ON STANDARD NORMAL CONVERGENCE OF THE MULTIVARIATE STUDENT t-STATISTIC FOR SYMMETRIC RANDOM VECTORS

It is proved that if the multivariate Student t-statistic based on i.i.d. symmetric random vectors is asymptotically standard normal, then these random vectors are in the generalized domain of attraction of the normal law. Uniform integrability is also considered, even in the absence of symmetry.

Computer Generation of Random Vectors from Continuous Multivariate Distributions

The evolution of statistical inference in the last years has been induced also by the development of new computational tools which have led both to the solution of classical statistical problems and to the implementation of new methods of analysis. In this framework, a relevant computational tool is given by simulation which leads to deal with methodological problems not solvable analytically. ...

Autoregressive Gaussian Random Vectors of First Order

Estimation of Spectral Measure of Stable Random Vectors

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