Importance sampling is a classical Monte Carlo technique in which a random sample from one probability density, π1, is used to estimate an expectation with respect to another, π. The importance sampling estimator is strongly consistent and, as long as two simple moment conditions are satisfied, it obeys a central limit theorem (CLT). Moreover, there is a simple consistent estimator for the asym...

This paper presents a novel method to determine the complete velocity vector of a moving target using a single Synthetic Aperture Radar (SAR) sensor. The method exploits the structure of the returned echo from a moving target: in the slow-time frequency domain, it is a scaled and shifted replica of the antenna radiation pattern immersed in Gaussian noise; the scale and the shift are related wit...

Abstract: This paper is the first of two on the problem of estimating a function of a probability distribution from a finite set of samples of that distribution. In this paper a Bayesian analysis of this problem is presented, the optimal properties of the Bayes estimators are discussed, and as an example of the formalism, closed form expressions for the Bayes estimators for the moments of the S...

The problem of estimation of change-points in a sequence of Poisson random variables is approached by allowing the change-point to range over the continuous time interval (0; T). A maximum-likelihood point estimator is derived, along with a Bayesian-based interval estimator. Simulation studies connrm that the true coverage probability is close to the nominal one for several locations of the cha...

Summary We propose a new fully automatic and robust Bayesian method to estimate precise reliable model parameters describing the observed S-wave spectra. All spectra associated with each event are modelled jointly, using t-distribution as likelihood function together informative prior distributions for increased robustness against outliers extreme values. The includes noise combined empirical G...

In this paper we propose a consistent and asymptotically normal estimator (CAN) of intensities 1  , 2  for a queueing network with feedback (in which a job may return to previously visited nodes) with distribution-free inter-arrival and service times. Using this estimator and its estimated variance, some   100 1 %   asymptotic confidence intervals of intensities are constructed. Also boot...

A nonlinear wavelet shrinkage estimator was proposed in Huang and Lu (2000). Such an estimator combined the asymptotic equivalence to the best linear unbiased prediction and the Bayesian estimation in nonparametric mixed-effects models. In this article a data-driven GCV method is proposed to select hyperparameters. The proposed GCV method has low computational cost and can be applied to one or ...

This paper presents a novel method to determine the complete velocity vector of a moving target using a single Synthetic Aperture Radar (SAR) sensor. The method exploits the structure of the returned echo from a moving target: in the slow-time frequency domain, it is a scaled and shifted replica of the antenna radiation pattern immersed in Gaussian noise; the scale and the shift are related wit...

It is proposed to jointly estimate the parameters of nonGaussian autoregressive (AR) processes in a Bayesian context using the Gibbs sampler. Using the Markov chains produced by the sampler an approximation to the vector MAP estimator is implemented. The results reported here used AR(4) models driven by noise sequences where each sample is iid as a two component Gaussian sum mixture. The result...