Models for Synthetic Aperture Radar Image Analysis

After reviewing some classical statistical hypothesis commonly used in image processing and analysis, this paper presents some models that are useful in synthetic aperture radar (SAR) image analysis

Bias and skewness in a general extreme-value regression model

In this paper we introduce a general extreme-value regression model and derive Cox and Snell's (1968) general formulae for second-order biases of maximum likelihood estimates (MLEs) of the parameters. We obtain formulae which can be computed by means of weighted linear regressions. Furthermore, we give the skewness of order n-1/2 of the maximum likelihood estimators of the parameters by using Bowman and Shenton's (1988) formula. A simulation study with results obtained with the use of Cox and Snell's (1968) formulae is discussed. Practical uses of this model and of the derived formulae for bias correction are also presented.Extreme-value regression model Dispersion covariates Maximum likelihood estimates Bias correction Skewness

Some restriction tests in a new class of regression models for proportions

The main purpose of this work is to study the behaviour of Skovgaard's [Skovgaard, I.M., 2001. Likelihood asymptotics. Scandinavian Journal of Statistics 28, 3-32] adjusted likelihood ratio statistic in testing simple hypothesis in a new class of regression models proposed here. The proposed class of regression models considers Dirichlet distributed observations, and the parameters that index the Dirichlet distributions are related to covariates and unknown regression coefficients. This class is useful for modelling data consisting of multivariate positive observations summing to one and generalizes the beta regression model described in Vasconcellos and Cribari-Neto [Vasconcellos, K.L.P., Cribari-Neto, F., 2005. Improved maximum likelihood estimation in a new class of beta regression models. Brazilian Journal of Probability and Statistics 19, 13-31]. We show that, for our model, Skovgaard's adjusted likelihood ratio statistics have a simple compact form that can be easily implemented in standard statistical software. The adjusted statistic is approximately chi-squared distributed with a high degree of accuracy. Some numerical simulations show that the modified test is more reliable in finite samples than the usual likelihood ratio procedure. An empirical application is also presented and discussed.