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Use of Auxiliary Information in Estimating the Finite Population Mean in Survey Sampling
-
Housila P. Singh
and
Unnati Bhayare
Published/Copyright:
June 6, 2011
Abstract
This paper considers the problem of estimating the mean μY of a finite population of the study variable Y using information on an auxiliary variable X. Motivated by Singh and Ruiz Espejo two classes of estimators (product-to-product and ratio-to-ratio) are proposed and their properties under large sample approximation are derived. Double sampling versions of the suggested classes of estimators are also presented. Numerical studies are used to demonstrate the performance of the suggested estimators compared with other estimators.
Keywords.: Study Variable; Auxiliary Variable; Random Sampling Without Replacement; Population Mean; Bias; Mean Square Error
Received: 2010-06-11
Published Online: 2011-06-06
Published in Print: 2011-September
© de Gruyter 2011
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- Editorial
- Control Charts Based on the g-and-h Distribution
- Economic Reliability Group Acceptance Sampling Plans Based on the Inverse-Rayleigh and the Log-Logistic Distributions
- Use of Auxiliary Information in Estimating the Finite Population Mean in Survey Sampling
- One-Sided Cumulative Sum (CUSUM) Control Charts for the Zero-Truncated Binomial Distribution
- Significance Test for the Half Logistic Distribution
- The Quality Loss Index QLI and Its Properties
- Statistical Quality Control Limits for the Sample Mean Chart Using Robust Extreme Ranked Set Sampling
Keywords for this article
Study Variable;
Auxiliary Variable;
Random Sampling Without Replacement;
Population Mean;
Bias;
Mean Square Error
Articles in the same Issue
- Editorial
- Control Charts Based on the g-and-h Distribution
- Economic Reliability Group Acceptance Sampling Plans Based on the Inverse-Rayleigh and the Log-Logistic Distributions
- Use of Auxiliary Information in Estimating the Finite Population Mean in Survey Sampling
- One-Sided Cumulative Sum (CUSUM) Control Charts for the Zero-Truncated Binomial Distribution
- Significance Test for the Half Logistic Distribution
- The Quality Loss Index QLI and Its Properties
- Statistical Quality Control Limits for the Sample Mean Chart Using Robust Extreme Ranked Set Sampling
