The present  paper agrees  with estimation of scale parameter θ of the Inverted Gamma (IG) Distribution when the shape parameter α is known (α=1), bypreliminarytestsinglestage shrinkage estimators using  suitable  shrinkage weight factor and region.  The expressions for the Bias, Mean Squared Error [MSE] for the proposed estimators are derived. Comparisons between the considered estimator with the usual estimator (MLE) and with the existing estimator  are performed .The results are presented in attached tables.

     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

In this paper we generalize Jacobsons results by proving that any integer  in   is a square-free integer), belong to . All units of  are generated by the fundamental unit  having the forms

Our generalization build on using the conditions

This leads us to classify the real quadratic fields  into the sets  Jacobsons results shows that  and Sliwa confirm that  and  are the only real quadratic fields in .

A field experiment was carried out at the research station of the College of Agriculture - Wasit University / Kut, during the fall season 2021 in soil with texture (sandy mixture) using the RCBD design in the arrangement of splintered plates and with three replications, to study the effect of spraying different combinations of organic emulsion (Appetizer) and NPK nano fertilizer with urea fertilizer on the growth of synthetic cultivars of yellow corn. The main panels included three synthetic varieties of yellow corn (Fajr1, Sumer and Baghdad3), which symbolized by (V1,V2,V3) in sequence, while the secondary panels included five fertilization treatments in which mineral fertilizer (urea) was used 46% nitrogen with the full recomme

Abstract

       There are many uncertainty sources that may affect the statistical reasoning. However, traditional methods can not deal with all kinds of uncertainty sources, which has led many researchers to develop traditional methods. Studies still exist to this day, making hypotheses to create a common understanding for the purpose of reaching new solutions through the use of new methods that combine traditional and modern theories of sources of uncertainty

       The aim of current study was to develop the adaptive fuzzy linear regression model in the case of using inaccurate data as the source of uncertainty. Specifically, the

In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function lnL(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Anot

Recently Genetic Algorithms (GAs) have frequently been used for optimizing the solution of estimation problems. One of the main advantages of using these techniques is that they require no knowledge or gradient information about the response surface. The poor behavior of genetic algorithms in some problems, sometimes attributed to design operators, has led to the development of other types of algorithms. One such class of these algorithms is compact Genetic Algorithm (cGA), it dramatically reduces the number of bits reqyuired to store the poulation and has a faster convergence speed. In this paper compact Genetic Algorithm is used to optimize the maximum likelihood estimator of the first order moving avergae model MA(1). Simulation results

This paper is interested in comparing the performance of the traditional methods to estimate parameter of exponential distribution (Maximum Likelihood Estimator, Uniformly Minimum Variance Unbiased Estimator) and the Bayes Estimator in the case of data to meet the requirement of exponential distribution and in the case away from the distribution due to the presence of outliers (contaminated values). Through the employment of simulation (Monte Carlo method) and the adoption of the mean square error (MSE) as criterion of statistical comparison between the performance of the three estimators for different sample sizes ranged between small, medium and large        (n=5,10,25,50,100) and different cases (wit