论文标题
瑞利回归模型的改进点估计
Improved Point Estimation for the Rayleigh Regression Model
论文作者
论文摘要
最近提出了瑞利回归模型，用于建模合成孔径雷达（SAR）图像像素的振幅值。但是，此类模型的推论基于最大似然估计器，这对于较小的信号长度可能会偏差。 SAR图像的瑞利回归模型通常会考虑到小像素窗口，这可能导致结果不准确。在这封信中，我们基于以下方式介绍了针对瑞利回归模型量身定制的偏置调整的估计器；（i）Cox和Snell的方法； （ii）FIRTH的计划； （iii）参数引导法。我们提出了考虑合成和实际SAR数据集的数值实验。偏置调整后的估计器产生几乎公正的估计和准确的建模结果。
The Rayleigh regression model was recently proposed for modeling amplitude values of synthetic aperture radar (SAR) image pixels. However, inferences from such model are based on the maximum likelihood estimators, which can be biased for small signal lengths. The Rayleigh regression model for SAR images often takes into account small pixel windows, which may lead to inaccurate results. In this letter, we introduce bias-adjusted estimators tailored for the Rayleigh regression model based on: (i) the Cox and Snell's method; (ii) the Firth's scheme; and (iii) the parametric bootstrap method. We present numerical experiments considering synthetic and actual SAR data sets. The bias-adjusted estimators yield nearly unbiased estimates and accurate modeling results.
