Name
Abstract | ||
|---|---|---|
During preceding theory study and engineering application, we dealed with the parameter estimation of one-dimension long memory process actually, and rarely take into account high dimensions. There are few papers about it. In this paper, using the decorrelation property of discrete wavelet transform, high dimension situation (mainly 2D) is simplified to 1D and corresponding referrers are improved according to new idea, combining with matrix transform. So the computation complexity is reduced effectively and estimation precision is satisfied. Some experiment results show that this algorithm has a better general performance. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72395-0_95 | ISNN (3) |
Keywords | Field | DocType |
engineering application,multidimension time series,multiscale estimation,parameter estimation,high dimension situation,estimation precision,computation complexity,experiment result,discrete wavelet,decorrelation property,account high dimension,corresponding referrers,computational complexity,discrete wavelet transform,satisfiability,time series | Decorrelation,Computer science,Memory process,Discrete wavelet transform,Artificial intelligence,Estimation theory,Transformation matrix,Stationary wavelet transform,Long memory,Computation complexity,Machine learning | Conference |
Volume | ISSN | Citations |
4493 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chenglin Wen | 1 | 179 | 42.72 |
| Guang-Jiang Wang | 2 | 1 | 1.06 |
| Chuanbo Wen | 3 | 0 | 1.01 |
| Chen Zhi-Guo | 4 | 1 | 2.04 |