Approach for extending sequence memory based on fractionally differenced model
Based on the decorrelation and frequency-band partitions of DWT for long memory process （LMP）, a ap-proximating covariance matrix was given and a related transform was constructed, which could transform an IID stochas-tic vector into a sample realization of fractionally differenced （FD） process. To...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2006-01-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/74661025/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841537399749869568 |
|---|---|
| author | JIA Dai-ping1 FAN Hong-da2 |
| author_facet | JIA Dai-ping1 FAN Hong-da2 |
| author_sort | JIA Dai-ping1 |
| collection | DOAJ |
| description | Based on the decorrelation and frequency-band partitions of DWT for long memory process （LMP）, a ap-proximating covariance matrix was given and a related transform was constructed, which could transform an IID stochas-tic vector into a sample realization of fractionally differenced （FD） process. To carry out this transform, a physical quan-tity called sub-band variance was proposed and computed under the FD parameters specified. At last, construction sam-ples were compared with theoretical model, and their statistical properties were checked in two ways. Computing results validate that the algorithm presented is feasible, rational and effective in a statistical sense. |
| format | Article |
| id | doaj-art-9ce0197b05384363be7ee8d8e645a7cc |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2006-01-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-9ce0197b05384363be7ee8d8e645a7cc2025-01-14T08:37:44ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2006-01-01667074661025Approach for extending sequence memory based on fractionally differenced modelJIA Dai-ping1FAN Hong-da2Based on the decorrelation and frequency-band partitions of DWT for long memory process （LMP）, a ap-proximating covariance matrix was given and a related transform was constructed, which could transform an IID stochas-tic vector into a sample realization of fractionally differenced （FD） process. To carry out this transform, a physical quan-tity called sub-band variance was proposed and computed under the FD parameters specified. At last, construction sam-ples were compared with theoretical model, and their statistical properties were checked in two ways. Computing results validate that the algorithm presented is feasible, rational and effective in a statistical sense.http://www.joconline.com.cn/zh/article/74661025/stochastic sequenceextending memorysub-band variancefractionally differencingpyramid algorithmparameter estimation |
| spellingShingle | JIA Dai-ping1 FAN Hong-da2 Approach for extending sequence memory based on fractionally differenced model Tongxin xuebao stochastic sequence extending memory sub-band variance fractionally differencing pyramid algorithm parameter estimation |
| title | Approach for extending sequence memory based on fractionally differenced model |
| title_full | Approach for extending sequence memory based on fractionally differenced model |
| title_fullStr | Approach for extending sequence memory based on fractionally differenced model |
| title_full_unstemmed | Approach for extending sequence memory based on fractionally differenced model |
| title_short | Approach for extending sequence memory based on fractionally differenced model |
| title_sort | approach for extending sequence memory based on fractionally differenced model |
| topic | stochastic sequence extending memory sub-band variance fractionally differencing pyramid algorithm parameter estimation |
| url | http://www.joconline.com.cn/zh/article/74661025/ |
| work_keys_str_mv | AT jiadaiping1 approachforextendingsequencememorybasedonfractionallydifferencedmodel AT fanhongda2 approachforextendingsequencememorybasedonfractionallydifferencedmodel |