| Title:
|
Efficient measurement of higher-order statistics of stochastic processes (English) |
| Author:
|
Magiera, Wladyslaw |
| Author:
|
Libal, Urszula |
| Author:
|
Wielgus, Agnieszka |
| Language:
|
English |
| Journal:
|
Kybernetika |
| ISSN:
|
0023-5954 (print) |
| ISSN:
|
1805-949X (online) |
| Volume:
|
54 |
| Issue:
|
5 |
| Year:
|
2018 |
| Pages:
|
865-887 |
| Summary lang:
|
English |
| . |
| Category:
|
math |
| . |
| Summary:
|
This paper is devoted to analysis of block multi-indexed higher-order covariance matrices, which can be used for the least-squares estimation problem. The formulation of linear and nonlinear least squares estimation problems is proposed, showing that their statements and solutions lead to generalized `normal equations', employing covariance matrices of the underlying processes. Then, we provide a class of efficient algorithms to estimate higher-order statistics (generalized multi-indexed covariance matrices), which are necessary taking in mind practical aspects of the nonlinear treatment of the least-squares estimation problem. The algorithms are examined for different higher-order and non-Gaussian processes (time-series) and an impact of signal properties on covariance matrices is analysed. (English) |
| Keyword:
|
covariance matrix |
| Keyword:
|
higher-order statistics |
| Keyword:
|
adaptive |
| Keyword:
|
nonlinear |
| MSC:
|
15B05 |
| MSC:
|
15B51 |
| MSC:
|
60G10 |
| MSC:
|
60G15 |
| MSC:
|
93E24 |
| idZBL:
|
Zbl 07031749 |
| idMR:
|
MR3893125 |
| DOI:
|
10.14736/kyb-2018-5-0865 |
| . |
| Date available:
|
2018-12-10T10:09:45Z |
| Last updated:
|
2020-01-05 |
| Stable URL:
|
http://hdl.handle.net/10338.dmlcz/147531 |
| . |
| Reference:
|
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| Reference:
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| Reference:
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| Reference:
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| Reference:
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| Reference:
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| . |
