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

作　　者：Christopher Onyema Arimie Iheanyi Sylvester Iwueze Maxwell Azubuike Ijomah Elechi Onyemachi 

机构地区：[1]Department of Mathematics and Statistics, University of Portharcourt, Portharcourt, Nigeria [2]Department of Statistics, Federal University of Technology, Owerri, Nigeria

出　　处：《Open Journal of Statistics》2018年第3期562-583,共22页统计学期刊（英文）

摘　　要：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.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.

关 键 词：White Noise Process NORMALITY Stationarity INVERTIBILITY COVARIANCE Structure 

分 类 号：O1[理学—数学]