Bias-corrected estimation for $$\mathcal{G}^0_I$$ G I 0 regression with applications

M. F. S. S. Sousa (), J. M. Vasconcelos () and A. D. C. Nascimento ()
Additional contact information
M. F. S. S. Sousa: Universidade Federal de Pernambuco
J. M. Vasconcelos: Universidade Federal Rural de Pernambuco
A. D. C. Nascimento: Universidade Federal de Pernambuco

AStA Advances in Statistical Analysis, 2025, vol. 109, issue 3, No 8, 557-589

Abstract: Abstract Synthetic aperture radar (SAR) systems are highly efficient tools for addressing remote sensing challenges. They offer several advantages, such as operating independently of atmospheric conditions and producing high spatial resolution images. However, SAR images are often contaminated by a type of interference called speckle noise, which complicates their analysis and processing. Therefore, proposing statistical methods, such as regression models, that account for speckle behavior is an important step for users of SAR systems. In the work [ISPRS J. Photogramm. Remote Sens., 213, 1–13, 2024], the $${\mathcal{G}^{0}_{I}}$$ G I 0 regression model (short for $$\mathcal{R} {\mathcal{G}^{0}_{I}}$$ R G I 0 ) was proposed as an interpretable tool to relate SAR intensity features to other physical properties. The authors employed maximum likelihood estimators (MLEs), known for their good asymptotic properties but prone to considerable bias in small and medium sample sizes. In this paper, we propose a matrix expression for the second-order bias of MLEs for $$\mathcal{R} {\mathcal{G}^{0}_{I}}$$ R G I 0 parameters, based on the Cox and Snell method. This proposal is justified by the necessity of using small and moderate windows when processing SAR images, such as for classification and filtering purposes. We compare bias-corrected MLEs with their counterparts using both Monte Carlo experiments and an application to SAR data from a Brazilian region. Numerical evidence demonstrates the effectiveness of our proposal.

Keywords: $$\mathcal{G}^{0}_{I}$$ G I 0 distribution; $$\mathcal{G}^{0}_{I}$$ G I 0 regression; Bias correction; SAR imagery (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10182-025-00525-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:109:y:2025:i:3:d:10.1007_s10182-025-00525-6

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10182/PS2

DOI: 10.1007/s10182-025-00525-6

Access Statistics for this article

AStA Advances in Statistical Analysis is currently edited by Göran Kauermann and Yarema Okhrin

More articles in AStA Advances in Statistical Analysis from Springer, German Statistical Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().