Milton, J.S. and Jesse, C.A. (1995) Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences. McGraw-Hill Inc., New York.

has been cited by the following article:

• TITLE: On the Use of Second and Third Moments for the Comparison of Linear Gaussian and Simple Bilinear White Noise Processes

  AUTHORS: Christopher Onyema Arimie, Iheanyi Sylvester Iwueze, Maxwell Azubuike Ijomah, Elechi Onyemachi

  KEYWORDS: White Noise Process, Normality, Stationarity, Invertibility, Covariance Structure

  JOURNAL NAME: Open Journal of Statistics, Vol.8 No.3, June 15, 2018

  ABSTRACT: The linear Gaussian white noise process (LGWNP) is an independent and identically distributed (iid) sequence with zero mean and finite variance with distribution . Some processes, such as the simple bilinear white noise process (SBWNP), have the same covariance structure like the LGWNP. How can these two processes be distinguished and/or compared? If is a realization of the SBWNP. This paper studies in detail the covariance structure of . It is shown from this study that; 1) the covariance structure of is non-normal with distribution equivalent to the linear ARMA(2, 1) model; 2) the covariance structure of is iid; 3) the variance of can be used for comparison of SBWNP and LGWNP.