Asymptotic normality of kernel estimates in a regression model for random fields
Overview & Synopsis
We establish the asymptotic normality of the regression estimator in a fixed-design setting when the errors are given by a field of dependent random variables. The result applies to martingale-difference or strongly mixing random fields. On this basis, a statistical test that can be applied to image analysis is also presented.
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Document Information
| Document Title | Asymptotic normality of kernel estimates in a regression model for random fields |
|---|---|
| Author / Organization | Mohamed El Machkouri, Radu Stoica |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2009 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-154696 |
| Original Source Link | Visit Source Page on hal.science » |