Estimation of a finite population total in varying probability sampling for multi-character surveys

Metrika ◽  
2001 ◽  
Vol 54 (2) ◽  
pp. 159-177 ◽  
Author(s):  
Raghunath Arnab
Keyword(s):  
Finite Population ◽  
Population Total ◽  
Probability Sampling

Estimation in Multi-Stage Sampling with Partially Missing First Stage Units

2005 ◽  
Vol 56 (1-4) ◽  
pp. 113-124
Author(s):  
Arijit Chaudhuri
Keyword(s):  
Practical Problem ◽  
Finite Population ◽  
Population Total ◽  
Probability Sampling ◽  
Multi Stage

Summary It is a practical problem to suitably estimate a finite population total providing an estimate of its measure of error when a sample is taken in multistages but some of the chosen first-stage units cannot be covered. Presuming the misses to occur at random, certain estimators of total along with estimators of their measures uf error are derived covering varying probability sampling.

scholarly journals On Comparison of Local Polynomial Regression Estimators for P=0 and P=1 in a Model Based Framework

2018 ◽  
Vol 7 (4) ◽  
pp. 104
Author(s):  
Conlet Biketi Kikechi ◽  
Richard Onyino Simwa
Keyword(s):  
Finite Population ◽  
Polynomial Regression ◽  
Asymptotic Properties ◽  
Asymptotic Relative Efficiency ◽  
Regression Estimator ◽  
Local Polynomial Regression ◽  
Linear Quadratic ◽  
Population Total ◽  
Local Polynomial ◽  
Model Based

This article discusses the local polynomial regression estimator for  and the local polynomial regression estimator for  in a finite population. The performance criterion exploited in this study focuses on the efficiency of the finite population total estimators. Further, the discussion explores analytical comparisons between the two estimators with respect to asymptotic relative efficiency. In particular, asymptotic properties of the local polynomial regression estimator of finite population total for  are derived in a model based framework. The results of the local polynomial regression estimator for  are compared with those of the local polynomial regression estimator for  studied by Kikechi et al (2018). Variance comparisons are made using the local polynomial regression estimator  for  and the local polynomial regression estimator  for  which indicate that the estimators are asymptotically equivalently efficient. Simulation experiments carried out show that the local polynomial regression estimator  outperforms the local polynomial regression estimator  in the linear, quadratic and bump populations.

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