Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution Model
This paper considers a multichannel deconvolution model with Gaussian white noises. The goal is to estimate the d-th derivatives of an unknown function in the model. For super-smooth case, we construct an adaptive linear wavelet estimator by wavelet projection method. For regular-smooth case, we pro...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Function Spaces |
| Online Access: | http://dx.doi.org/10.1155/2022/2075229 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849409291942887424 |
|---|---|
| author | Huijun Guo Shuzi Li |
| author_facet | Huijun Guo Shuzi Li |
| author_sort | Huijun Guo |
| collection | DOAJ |
| description | This paper considers a multichannel deconvolution model with Gaussian white noises. The goal is to estimate the d-th derivatives of an unknown function in the model. For super-smooth case, we construct an adaptive linear wavelet estimator by wavelet projection method. For regular-smooth case, we provide an adaptive nonlinear wavelet estimator by hard-thresholded method. In order to measure the global performances of our estimators, we show upper bounds on convergence rates using the Lp-risk (1≤p<∞). |
| format | Article |
| id | doaj-art-7e1b3d5648954edba50cbde0d14951b1 |
| institution | Kabale University |
| issn | 2314-8888 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Function Spaces |
| spelling | doaj-art-7e1b3d5648954edba50cbde0d14951b12025-08-20T03:35:33ZengWileyJournal of Function Spaces2314-88882022-01-01202210.1155/2022/2075229Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution ModelHuijun Guo0Shuzi Li1School of Mathematics and Computational ScienceSchool of Mathematics and Computational ScienceThis paper considers a multichannel deconvolution model with Gaussian white noises. The goal is to estimate the d-th derivatives of an unknown function in the model. For super-smooth case, we construct an adaptive linear wavelet estimator by wavelet projection method. For regular-smooth case, we provide an adaptive nonlinear wavelet estimator by hard-thresholded method. In order to measure the global performances of our estimators, we show upper bounds on convergence rates using the Lp-risk (1≤p<∞).http://dx.doi.org/10.1155/2022/2075229 |
| spellingShingle | Huijun Guo Shuzi Li Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution Model Journal of Function Spaces |
| title | Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution Model |
| title_full | Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution Model |
| title_fullStr | Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution Model |
| title_full_unstemmed | Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution Model |
| title_short | Wavelet Estimation of Function Derivatives from a Multichannel Deconvolution Model |
| title_sort | wavelet estimation of function derivatives from a multichannel deconvolution model |
| url | http://dx.doi.org/10.1155/2022/2075229 |
| work_keys_str_mv | AT huijunguo waveletestimationoffunctionderivativesfromamultichanneldeconvolutionmodel AT shuzili waveletestimationoffunctionderivativesfromamultichanneldeconvolutionmodel |