Output distributions and covariance functions of certain non-linear transformations
Three specific non-linear transformations of Gaussian stochastic processes occurring in signal detection and control theory are considered. The Gaussian stochastic processes are not necessarily stationary. The common feature of the transformed stochastic processes is that the determination of their...
Saved in:
Main Author: | |
---|---|
Format: | Article |
Published: |
Taylor & Francis
1971
|
Subjects: | |
Online Access: | http://eprints.um.edu.my/24473/ https://doi.org/10.1080/00207177108932006 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.um.eprints.24473 |
---|---|
record_format |
eprints |
spelling |
my.um.eprints.244732021-03-24T03:21:10Z http://eprints.um.edu.my/24473/ Output distributions and covariance functions of certain non-linear transformations Cheng, M.C. QA Mathematics Three specific non-linear transformations of Gaussian stochastic processes occurring in signal detection and control theory are considered. The Gaussian stochastic processes are not necessarily stationary. The common feature of the transformed stochastic processes is that the determination of their covariance functions depends upon the evaluation of the orthant probability of four Gaussian variates over the respective correlation matrix. A method for evaluating this orthant probability, when the correlation matrix has certain specific forms, has recently been discussed by Cheng (1969). The application of this method yields closed-form expressions, in terms of tabulated functions, for the output probability distributions and covariance functions of the non-linear transformations investigated. © 1970 Taylor & Francis Group, LLC. Taylor & Francis 1971 Article PeerReviewed Cheng, M.C. (1971) Output distributions and covariance functions of certain non-linear transformations. International Journal of Control, 13 (6). pp. 1065-1071. ISSN 0020-7179 https://doi.org/10.1080/00207177108932006 doi:10.1080/00207177108932006 |
institution |
Universiti Malaya |
building |
UM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaya |
content_source |
UM Research Repository |
url_provider |
http://eprints.um.edu.my/ |
topic |
QA Mathematics |
spellingShingle |
QA Mathematics Cheng, M.C. Output distributions and covariance functions of certain non-linear transformations |
description |
Three specific non-linear transformations of Gaussian stochastic processes occurring in signal detection and control theory are considered. The Gaussian stochastic processes are not necessarily stationary. The common feature of the transformed stochastic processes is that the determination of their covariance functions depends upon the evaluation of the orthant probability of four Gaussian variates over the respective correlation matrix. A method for evaluating this orthant probability, when the correlation matrix has certain specific forms, has recently been discussed by Cheng (1969). The application of this method yields closed-form expressions, in terms of tabulated functions, for the output probability distributions and covariance functions of the non-linear transformations investigated. © 1970 Taylor & Francis Group, LLC. |
format |
Article |
author |
Cheng, M.C. |
author_facet |
Cheng, M.C. |
author_sort |
Cheng, M.C. |
title |
Output distributions and covariance functions of certain non-linear transformations |
title_short |
Output distributions and covariance functions of certain non-linear transformations |
title_full |
Output distributions and covariance functions of certain non-linear transformations |
title_fullStr |
Output distributions and covariance functions of certain non-linear transformations |
title_full_unstemmed |
Output distributions and covariance functions of certain non-linear transformations |
title_sort |
output distributions and covariance functions of certain non-linear transformations |
publisher |
Taylor & Francis |
publishDate |
1971 |
url |
http://eprints.um.edu.my/24473/ https://doi.org/10.1080/00207177108932006 |
_version_ |
1695531083164024832 |
score |
13.211869 |