Name
Abstract | ||
|---|---|---|
Using a wavelet basis, we establish in this paper upper bounds of wavelet estimation on \( L^{p}({\mathbb {R}}^{d}) \) risk of regression functions with strong mixing data for \( 1\le p<\infty \). In contrast to the independent case, these upper bounds have different analytic formulae for \(p\in [1, 2]\) and \(p\in (2, +\infty )\). For \(p=2\), it turns out that our result reduces to a theorem of Chaubey et al. (J Nonparametr Stat 25:53–71, 2013); and for \(d=1\) and \(p=2\), it becomes the corresponding theorem of Chaubey and Shirazi (Commun Stat Theory Methods 44:885–899, 2015). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10260-018-00430-0 | Statistical Methods and Applications |
Keywords | Field | DocType |
Regression estimation,$$L^{p}$$Lp risk,Convergence rate,Strong mixing,Wavelet,62G07,42C40,62G20 | Combinatorics,Regression,Rate of convergence,Statistics,Mathematics,Wavelet | Journal |
Volume | Issue | ISSN |
27 | 4 | 1618-2510 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junke Kou | 1 | 0 | 0.34 |
| Youming Liu | 2 | 7 | 2.68 |