Parameter Estimation Using the EM Algorithm for Symmetric Stable Random Variables and Sub-Gaussian Random Vectors

Parameter estimation for spatial random trees using the EM algorithm

A new class of multiscale multidimensional stochastic processes called spatial random trees was recently introduced in [9]. The model is based on multiscale stochastic trees with stochastic structure as well as stochastic states. In this work, we describe a method for estimating the parameters of the process.

Estimation of Spectral Measure of Stable Random Vectors

This article has no abstract.

Autoregressive Gaussian Random Vectors of First Order

Circularly-Symmetric Gaussian random vectors

A number of basic properties about circularly-symmetric Gaussian random vectors are stated and proved here. These properties are each probably well known to most researchers who work with Gaussian noise, but I have not found them stated together with simple proofs in the literature. They are usually viewed as too advanced or too detailed for elementary texts but are used (correctly or incorrect...

Bootstrap Tail Thickness Estimation for Symmetric Alpha-Stable Random Variables

A new theorem shows that a bootstrap algorithm can estimate the impulsivity or tail thickness of symmetric a-stable (SαS) signals. SαS bell curves include the Gaussian bell curve as a special but non-impulsive case. Signals grow more impulsive as the bell curve’s tail thickness increases or as the tail-thickness parameter α falls from 2 toward 0. The thin-tailed Gaussian bell curve has α = 2. T...