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Double Stage Shrinkage Estimator in Pareto Distribution
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Publication Date
Sun Dec 30 2012
Journal Name
Journal Of Kufa For Mathematics And Computer
On Jeffery Prior Distribution in Modified Double Stage Shrinkage-Bayesian Estimator for Exponential Mean
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Publication Date
Thu Feb 02 2012
Journal Name
Education College Journal/al-mustansiriyah University
On Significance Testimator in Pareto Distribution Via Shrinkage Technique
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In this paper, preliminary test Shrinkage estimator have been considered for estimating the shape parameter α of pareto distribution when the scale parameter equal to the smallest loss and when a prior estimate α0 of α is available as initial value from the past experiences or from quaintance cases. The proposed estimator is shown to have a smaller mean squared error in a region around α0 when comparison with usual and existing estimators.

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Publication Date
Sun Jan 02 2011
Journal Name
Education College Journal/al-mustansiriyah
Double Stage Shrinkage Estimators of Two Parameters Generalized Rayleigh Distribution
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Publication Date
Wed Jan 01 2014
Journal Name
American Journal Of Mathematics And Statistics
Preliminary Test Single Stage Shrinkage Estimator for the Scale Parameter of Gamma Distribution
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Publication Date
Thu Aug 25 2016
Journal Name
International Journal Of Mathematics Trends And Technology
Pretest Single Stage Shrinkage Estimator for the Shape Parameter of the Power Function Distribution
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Publication Date
Sun Mar 04 2012
Journal Name
Baghdad Science Journal
Double Stage Cumulative Shrunken Bayes Estimator for the variance of Normal distribution for equal volume of two sample
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In this article we study the variance estimator for the normal distribution when the mean is un known depend of the cumulative function between unbiased estimator and Bays estimator for the variance of normal distribution which is used include Double Stage Shrunken estimator to obtain higher efficiency for the variance estimator of normal distribution when the mean is unknown by using small volume equal volume of two sample .

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Publication Date
Tue Dec 01 2009
Journal Name
Journal Of Economics And Administrative Sciences
Estimation of the parameter of the Pareto distribution manual Using the general mediator estimator
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Estimation of the tail index parameter of a one - parameter Pareto model has wide important by the researchers because it has awide application in the econometrics science and reliability theorem.

Here we introduce anew estimator of "generalized median" type and compare it with the methods of Moments and Maximum likelihood by using the criteria, mean square error.

 The estimator of generalized median type performing best over all.

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Publication Date
Sun Dec 05 2010
Journal Name
Baghdad Science Journal
Pre-Test Single and Double Stage Shrunken Estimators for the Mean of Normal Distribution with Known Variance
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This paper is concerned with pre-test single and double stage shrunken estimators for the mean (?) of normal distribution when a prior estimate (?0) of the actule value (?) is available, using specifying shrinkage weight factors ?(?) as well as pre-test region (R). Expressions for the Bias [B(?)], mean squared error [MSE(?)], Efficiency [EFF(?)] and Expected sample size [E(n/?)] of proposed estimators are derived. Numerical results and conclusions are drawn about selection different constants included in these expressions. Comparisons between suggested estimators, with respect to classical estimators in the sense of Bias and Relative Efficiency, are given. Furthermore, comparisons with the earlier existing works are drawn.

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Publication Date
Sat Nov 01 2014
Journal Name
International Journal Of Statistics
Single and Double Stage Shrinkage Estimators for the Normal Mean with the Variance Cases
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Publication Date
Wed Jun 01 2011
Journal Name
Journal Of Economics And Administrative Sciences
Probabilistic Inventory Models With Pareto Distribution
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Inventory or inventories are stocks of goods being held for future use or sale. The demand for a product in is the number of units that will need to be removed from inventory for use or sale during a specific period. If the demand for future periods can be predicted with considerable precision, it will be reasonable to use an inventory rule that assumes that all predictions will always be completely accurate. This is the case where we say that demand is deterministic.

The timing of an order can be periodic (placing an order every days) or perpetual (placing an order whenever the inventory declines to units).

in this research we discuss how to  formulating inv

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