In the absence of relevant prior experience, popular Bayesian estimation techniques usually begin with some form of "uninformative" prior distribution intended to have minimal inferential influence. Bayes rule will still produce nice-looking estimates and credible intervals, but these lack the logical force attached to experience-based priors and require further justification. This paper concer...

Data analysis in MRI usually entails a series of processing procedures. One of these procedures is noise assessment, which in the context of this work, includes both the identification of noise-only pixels and the estimation of noise variance (standard deviation). Although noise assessment is critical to many MRI processing techniques, the identification of noise-only pixels has received less a...

We study some of the most commonly used mutual information estimators, based on histograms of fixed or adaptive bin size, k-nearest neighbors and kernels, and focus on optimal selection of their free parameters. We examine the consistency of the estimators (convergence to a stable value with the increase of time series length) and the degree of deviation among the estimators. The optimization o...

where "| A |" denotes the (fc-dimensional) volume of A. If the x¡ are regarded as independent (or at least pairwise independent) random variables, then the estimator / is a random variable whose mean is I and whose standard deviation is dN~112, where d2 = | A \ jA f2 — (JA f)2. (7 is the mean, and d is the standard deviation of the random variable | A ¡fix), where x is a random variable uniform...

This paper is devoted to "Some Discrete Distributions Generated by Standard Stable Densities" (in short, Discrete Stable Densities). The large-sample properties of M-estimators as obtained by the "Generalized Method of Moments" (GMM) are discussed for such distributions. Some corollaries are proposed. Moreover, using the respective results we demonstrate the large-sample pro...

The convexity arguments developed by Pollard [D. Pollard, Asymptotics for least absolute deviation regression estimators, Econometric Theory 7 (1991) 186–199], Hjort and Pollard [N.L. Hjort, D. Pollard, Asymptotics for minimizers of convex processes, 1993 (unpublished manuscript)], and Geyer [C.J. Geyer, On the asymptotics of convex stochastic optimization, 1996 (unpublished manuscript)] are no...

In this paper, we derive the in uence function of dispersion estimators, based on a scale approach. The relation between the gross-error sensitivity of dispersion estimators and the one of the underlying scale estimator is described. We show that for the bivariate Gaussian distributions, the asymptotic variance of covariance estimators is minimal in the independent case, and is strictly increas...