Second-order complex random vectors and normal distributions

Second-order complex random vectors and normal distributions

We formulate as a deconvolution problem thecausalhoncausal non-Gaussian multichannel autoregressive (AR)parameter estimation problem. The super exponential aljporithmpresented in a recent paper by Shalvi and Weinstein is generalizedto the vector case. We present an adaptive implementation that isvery attractive since it is higher order statistics (HOS) based b u t doesno...

Autoregressive Gaussian Random Vectors of First Order

autoregressive gaussian random vectors of first order

Stochastic Hill Climbing with Learning by Vectors of Normal Distributions Second Corrected and Enhanced Version

This paper describes a stochastic hill climbing algorithm named SHCLVND to optimize arbitrary vectorial < n ! < functions. It needs less parameters. It uses normal (Gaussian) distributions to represent probabilities which are used for generating more and more better argument vectors. The-parameters of the normal distributions are changed by a kind of Hebbian learning. Kvasnicka et al. KPP95] us...

Comparing Mean Vectors Via Generalized Inference in Multivariate Log-Normal Distributions

Abstract In this paper, we consider the problem of means in several multivariate log-normal distributions and propose a useful method called as generalized variable method. Simulation studies show that suggested method has a appropriate size and power regardless sample size. To evaluation this method, we compare this method with traditional MANOVA such that the actual sizes of the two methods ...