This paper is concerned with preliminary test double stage shrinkage estimators to estimate the variance (s²) of normal distribution when a prior estimate  of the actual value (s²) is a available when the mean is unknown  , using specifying shrinkage weight factors y(×) in addition to pre-test region (R).

      Expressions for the Bias, Mean squared error [MSE (×)], Relative Efficiency [R.EFF (×)], Expected sample size [E(n/s²)] and percentage of overall sample saved of proposed estimator were derived. Numerical results (using MathCAD program) and conclusions are drawn about selection of different constants including in the me

The analysis of survival and reliability considered of topics and methods of vital statistics at the present time because of their importance in the various demographical, medical, industrial and engineering fields. This research focused generate random data for samples from the probability distribution Generalized Gamma: GG, known as: "Inverse Transformation" Method: ITM, which includes the distribution cycle integration function incomplete Gamma integration making it more difficult classical estimation so will be the need to illustration to the method of numerical approximation and then appreciation of the function of survival function. It was estimated survival function by simulation the way "Monte Carlo". The Entropy method used for the

Carbon monoxide (CO) plays an important indirect greenhouse gases due to its influences on the budgets of hydroxyl radicals (OH) and Ozone (O3). The atmospheric carbon monoxide (CO) observations can only be made on global and continental scales by remote sensing instruments situated in space. One of instrument is the Measurements of Pollution in the Troposphere (MOPITT), which is designed to measure troposphere CO and CH4 by use of a nadir-viewing geometry and was launched aboard the Earth Observing System (EOS) Terra spacecraft on 18 December 1999. Results from the analysis of the retrieved monthly (1ºх1º) spatial grid resolution, from the MOPITT data were utilized to analyze the distribution of CO surface mixing ratio in Iraq for th

Abstract

            Rayleigh distribution is one of the important distributions used for analysis life time data, and has applications in reliability study and physical interpretations. This paper introduces four different methods to estimate the scale parameter, and also estimate reliability function; these methods are Maximum Likelihood, and Bayes and Modified Bayes, and Minimax estimator under squared error loss function, for the scale and reliability function of the generalized Rayleigh distribution are obtained. The comparison is done through simulation procedure, t

Methods of estimating statistical distribution have attracted many researchers when it comes to fitting a specific distribution to data. However, when the data belong to more than one component, a popular distribution cannot be fitted to such data. To tackle this issue, mixture models are fitted by choosing the correct number of components that represent the data. This can be obvious in lifetime processes that are involved in a wide range of engineering applications as well as biological systems. In this paper, we introduce an application of estimating a finite mixture of Inverse Rayleigh distribution by the use of the Bayesian framework when considering the model as Markov chain Monte Carlo (MCMC). We employed the Gibbs sampler and

In this paper, Bayes estimators for the shape and scale parameters of Weibull distribution have been obtained using the generalized weighted loss function, based on Exponential priors. Lindley’s approximation has been used effectively in Bayesian estimation. Based on theMonte Carlo simulation method, those estimators are compared depending on the mean squared errors (MSE’s).

A true random TTL pulse generator was implemented and investigated for quantum key distribution systems. The random TTL signals are generated by low cost components available in the local markets. The TTL signals are obtained by using true random binary sequences based on registering photon arrival time difference registered in coincidence windows between two single – photon detectors. The true random TTL pulse generator performance was tested by using time to digital converters which gives accurate readings for photon arrival time. The proposed true random pulse TTL generator can be used in any quantum -key distribution system for random operation of the transmitters for these systems

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.