scholarly journals Development of Estimation Procedure of Population Mean in Two-Phase Stratified Sampling

2020 ◽  
Author(s):  
Partha Parichha ◽  
Kajla Basu ◽  
Arnab Bandyopadhyay
Keyword(s):  
Estimation Procedure ◽  
Stratified Sampling ◽  
Two Phase ◽  
Population Mean

scholarly journals Two- Phase Stratified Sampling Estimator for Population Mean in The Presence of Nonresponse Using Single Auxiliary Variable

2020 ◽  
Vol 8 (2) ◽  
pp. 49-56
Author(s):  
Akan Anieting
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Variable ◽  
Stratified Sampling ◽  
Second Phase ◽  
Two Phase ◽  
Population Mean ◽  
Squared Error ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Phase Sample

In this article, a new estimator for population mean in two-phase stratified sampling in the presence of nonresponse using single auxiliary variable has been proposed. The bias and Mean Squared Error (MSE) of the proposed estimator has been given using large sample approximation. The empirical study shows that the MSE of the proposed estimator is more efficient than existing estimators. The optimum values of first and second phase sample have been determined.

scholarly journals Optimal sampling schemes based on the anticipated variance with lack of fit

2002 ◽  
Vol 32 (12) ◽  
pp. 2236-2243 ◽  
Author(s):  
D Mandallaz
Keyword(s):  
Estimation Procedure ◽  
Cluster Sampling ◽  
Second Phase ◽  
Optimal Sampling ◽  
Two Phase ◽  
Sampling Schemes ◽  
Lack Of Fit ◽  
Important Improvement ◽  
Phase Sampling ◽  
Simple Stochastic Model

This note presents an important improvement for optimal sampling schemes based on the anticipated variance. The anticipated variance is defined as the average of the design-based variance under a simple stochastic model in which the trees are assumed to be uniformly and independently distributed within a given number of so-called Poisson strata. We consider two-phase two-stage cluster sampling schemes in which costs and terrestrial second-phase sampling density can vary over domains. The estimation procedure is based on post-stratification with respect to so-called working strata that do not need to be identical with the Poisson strata, usually unknown, which induces a lack of fit. It is then possible to derive analytically the optimal sampling schemes. Data from the Swiss National Inventory illustrates the method.

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