Use of Some Known Values of Population Parameters for Estimating the Finite Population Mean for Random Non-response in Survey Sampling

Author(s):  
Sunil Kumar
2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Mursala Khan ◽  
Rajesh Singh

A chain ratio-type estimator is proposed for the estimation of finite population mean under systematic sampling scheme using two auxiliary variables. The mean square error of the proposed estimator is derived up to the first order of approximation and is compared with other relevant existing estimators. To illustrate the performances of the different estimators in comparison with the usual simple estimator, we have taken a real data set from the literature of survey sampling.


1998 ◽  
Vol 28 (10) ◽  
pp. 1429-1447 ◽  
Author(s):  
T G Gregoire

Model-based ideas in finite-population sampling have received renewed discussion in recent years.Their relationship to the classical ideas in sampling theorydo not appear to be universally well understood by samplers in applied disciplines such as forestry, and ecology more broadly.The two inferential paradigms are constrasted, andexplanations are supplemented with examples of discrete aswell as continuously distributed populations. The treatment of spatial structureis examined, also.


2015 ◽  
Vol 54 (1) ◽  
pp. 33-44
Author(s):  
Linas Naujanis ◽  
Danutė Krapavickaitė

Problems of finite population parameters estimation are analyzed in this paper. Four methods have been used for parameterestimation: sampling design-based unbiased estimator, multiple regression and logistic regression model-based estimators and James–Stein estimator. The design-based estimator is unbiased, but its standard deviation is usually high. Model-based estimators are notunbiased, but their standard deviations are low. In order to minimize the standard deviation and the bias, the James–Stein estimator isapplied. Labour force survey data of Statistics Lithuania are used for simulation to study model-based estimators for the number ofunemployed and employed persons in districts and counties, and the role of information on registered unemployment in these models.


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