Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Econometric foundations
Analysis of minute features in speckled imagery with maximum likelihood estimation
EURASIP Journal on Applied Signal Processing
Target detection in SAR images based on a level set approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Polarimetric SAR image segmentation with B-splines and a new statistical model
Multidimensional Systems and Signal Processing
Dealing with monotone likelihood in a model for speckled data
Computational Statistics & Data Analysis
A new similarity measure for nonlocal filtering in the presence of multiplicative noise
Computational Statistics & Data Analysis
Original Articles: Nonparametric edge detection in speckled imagery
Mathematics and Computers in Simulation
Alternatives to the usual likelihood ratio test in mixed linear models
Computational Statistics & Data Analysis
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This paper's aim is to evaluate the effectiveness of bootstrap methods in improving estimation of clutter properties in speckled imagery. Estimation is performed by standard maximum likelihood methods. We show that estimators obtained this way can be quite biased in finite samples, and develop bias correction schemes using bootstrap resampling. In particular, we propose a bootstrapping scheme which is an adaptation of that proposed by Efron (J. Amer. Statist. Assoc. 85 (1990) 79). The proposed bootstrap does not require the quantity of interest to have closed form, as does Effort's original proposal. The adaptation we suggest is particularly important since the maximum likelihood estimator of interest does not have a closed form. We show that this particular bootstrapping scheme outperforms alternative forms of bias reduction mechanisms, thus delivering more accurate inference. We also consider interval estimation using bootstrap methods, and show that a particular parametric bootstrap-based confidence interval is typically more reliable than both the asymptotic confidence interval and other bootstrap-based confidence intervals. An application to real data is presented and discussed.