The estimation of the total number of defects at early stages of the testing process helps managers to make resource allocation and deadline decisions. The use of nonbayesian approaches has proven to be accurate but presents a certain latency to achieve a reasonable accuracy. Here we describe BayesED3M , a bayesian estimator construct upon an existing MLE (Maximum Likelihood Estimator) named as...

• In the present paper we study the performance of the Bayes Shrinkage estimators for the scale parameter of the Weibull distribution under the squared error loss and the LINEX loss functions in the presence of a prior point information of the scale parameter when Type-II censored data are available. The properties of the minimax estimators are also discussed. Key-Words: • Bayes shrinkage estim...

The possibility of employing explicitly defined functions of the observations as estimators of parametric functions in nonlinear regression analysis is explored. A general theory of best average mean square error estimation leading to explicit estimators is set forth. Such estimators are given a Bayesian interpretation as Fourier expansions of the estimator which minimizes expected posterior sq...

This work presents a fusion of Log Gabor Wavelet (LGW) and Maximum a Posteriori (MAP) estimator as a speech enhancement tool for acoustical background noise reduction. The probability density function (pdf) of the speech spectral amplitude is approximated by a Generalized Laplacian Distribution (GLD). Compared to earlier estimators the proposed method estimates the underlying statistical model ...

The use of multicast inference on end-to-end measurement has recently been proposed as a means to infer network internal characteristics such as packet link loss rate and delay. In this paper, we propose three types of algorithm that use loss measurements to infer the underlying multicast topology: i) a grouping estimator that exploits the monotonicity of loss rates with increasing path length;...

We consider estimation in a sparse additive regression model with the design points on a regular lattice. We establish the minimax convergence rates over Sobolev classes and propose a Fourier-based rate optimal estimator which is adaptive to the unknown sparsity and smoothness of the response function. The estimator is derived within a Bayesian formalism but can be naturally viewed as a penaliz...

Error density estimation in a nonparametric functional regression model with functional predictor and scalar response is considered. The unknown error density is approximated by a mixture of Gaussian densities with means being the individual residuals, and variance as a constant parameter. This proposed mixture error density has a form of a kernel density estimator of residuals, where the regre...

The combination of generalization Type-I hybrid censoring and Type-II schemes, scheme creates a new called Unified scheme. Therefore, in this study, the E-Bayesian estimation parameters inverse Weibull (IW) distribution is obtained under unified scheme, efficiency proposed method was compared with Bayesian estimator using Monte Carlo simulation real data set.

A simultaneous multi-frame super-resolution video reconstruction procedure, utilizing spatio-temporal smoothness constraints and motion estimator confidence parameters is proposed. The ill-posed inverse problem of reconstructing super-resolved imagery from the low resolution, degraded observations is formulated as a statistical inference problem and a Bayesian, maximum a-posteriori (MAP) approa...

In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare th...