Calibration Approach-based Product Estimator of Finite Population Total with Subsampling of Nonrespondents under Single and Two-phase Sampling

2015 ◽  
Vol 45 (8) ◽  
pp. 2965-2980
Author(s):  
Rohan Kumar Raman ◽  
U. C. Sud ◽  
Hukum Chandra
Keyword(s):  
Finite Population ◽  
Two Phase ◽  
Population Total ◽  
Phase Sampling ◽  
Product Estimator ◽  
Two Phase Sampling ◽  
Calibration Approach

A Class of Estimators for a Finite Population Mean Based on Multivariate Information and General two Phase Sampling

1995 ◽  
Vol 45 (3-4) ◽  
pp. 203-218 ◽  
Author(s):  
T. P. Tripathi ◽  
M. S. Ahmed
Keyword(s):  
Finite Population ◽  
Population Data ◽  
Two Phase ◽  
Population Means ◽  
Population Mean ◽  
Phase Sampling ◽  
Optimum Estimator ◽  
Class Of Estimators ◽  
Relative Gains ◽  
Two Phase Sampling

A class of estimators for a finite population mean is presented for the situations where population means of some auxiliary variables are known while those of others are unknown. The results for general two phase sampling are indicated while the detailed discussion is made for the case when SRSWOR is used at both the phases. While several known estimators belong to the proposed clas~ some new estimators are identified as well. The optimum estimator in the proposed class is found to be better than the so-called chain ratio and regression estimators discu ssed by Chand (1975). Kiregyera (1984) and Mukerjee et al. (1987). The relative gains in efficiency of tho proposed optimum estimator over the others are obtained for a natural population data and found to be quite appreciable.

scholarly journals A Study on the Chain Ratio-Type Estimator of Finite Population Variance

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yunusa Olufadi ◽  
Cem Kadilar
Keyword(s):  
Finite Population ◽  
Numerical Comparison ◽  
Mean Square ◽  
Auxiliary Variables ◽  
Population Variance ◽  
Two Phase ◽  
First Order ◽  
Phase Sampling ◽  
The Mean ◽  
Two Phase Sampling

We suggest an estimator using two auxiliary variables for the estimation of the unknown population variance. The bias and the mean square error of the proposed estimator are obtained to the first order of approximations. In addition, the problem is extended to two-phase sampling scheme. After theoretical comparisons, as an illustration, a numerical comparison is carried out to examine the performance of the suggested estimator with several estimators.

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