Two Properties of Vectors of Quadratic Forms in Gaussian Random Variables

Autoregressive Gaussian Random Vectors of First Order

Some Probability Inequalities for Quadratic Forms of Negatively Dependent Subgaussian Random Variables

In this paper, we obtain the upper exponential bounds for the tail probabilities of the quadratic forms for negatively dependent subgaussian random variables. In particular the law of iterated logarithm for quadratic forms of independent subgaussian random variables is generalized to the case of negatively dependent subgaussian random variables.

autoregressive gaussian random vectors of first order

A tail inequality for quadratic forms of subgaussian random vectors

This article proves an exponential probability tail inequality for positive semidefinite quadratic forms in a subgaussian random vector. The bound is analogous to one that holds when the vector has independent Gaussian entries.

I the Distributions of Quadratic Forms on Normal Random Variables