Model Estimated Building in Finite Population Sampling

Authors

  • صباح هادي
  • سليم الغرابي
  • ايمان محمد

DOI:

https://doi.org/10.33095/jeas.v22i87.722

Keywords:

مجتمع محدود المعاينة، معدل المجتمع، التعدد الخطي، انحدار الحرف، التقديرات الحصينة., Finite Population Sampling, Average of Population, Multi collinearity, Ridge Regression, Robust Estimation.

Abstract

Abstract

The population is sets of vocabulary common in character or characters and it’s study subject or research . statistically , this sets is called study population (or abridgement population ) such as set of person or trees of special kind of fruits or animals or product  any country for any commodity through infinite temporal period term ... etc.

The population maybe finite if we can enclose the number of its members such as the students of finite school grade . and maybe infinite if we can not enclose the number of it is members such as stars or aquatic creatures in the sea . when we study any character for population the statistical data is concentrate by two method , the first method is census which we concentrate the data for each singular of population , and the second method is sampling method which we concentrate the data for part of population such as this part (sample) have the sane characters of population which we taken .

            This research proposes estimation for some of parameters in finite population sampling, such we use the estimation of average of the model  and obtaining of the Best Unbiased Estimator of average of finite population by the Fuller use. This research also proposes some robust estimators of the finite population mean which suitable in the presence of some outlying observations. The robust estimators are derived on the basis of certain predictive influence functions.

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Published

2016-02-01

Issue

Section

Statistical Researches

How to Cite

“Model Estimated Building in Finite Population Sampling” (2016) Journal of Economics and Administrative Sciences, 22(87), p. 384. doi:10.33095/jeas.v22i87.722.

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