scholarly journals Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model-Based Approach

2016 ◽  
Vol 06 (06) ◽  
pp. 1085-1097
Author(s):  
Charles K. Syengo ◽  
Sarah Pyeye ◽  
George O. Orwa ◽  
Romanus O. Odhiambo
Keyword(s):  
Random Sampling ◽  
Finite Population ◽  
Polynomial Regression ◽  
Stratified Random Sampling ◽  
Regression Estimator ◽  
Local Polynomial Regression ◽  
Population Total ◽  
Local Polynomial ◽  
Model Based

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.

scholarly journals Comparison of Various Methods for Estimating Finite Population Total in Survey Sampling When Study Variable and Auxiliary Variable are Inversely Related

2019 ◽  
Vol 11 (1) ◽  
pp. 15-22
Author(s):  
S. Kumar ◽  
B. V. S. Sisodia
Keyword(s):  
Simulation Study ◽  
Finite Population ◽  
Auxiliary Variable ◽  
Survey Sampling ◽  
The Other ◽  
Regression Estimator ◽  
Study Variable ◽  
Population Total ◽  
Model Based ◽  
Proposed Model

In the present paper, a model based calibration estimator of population total has been developed when study variable y and auxiliary variable x are inversely related. The relative performance of the proposed model based calibration estimator in comparison to model based estimator, the usual regression estimator and calibration based regression estimator have been examined by conducting a limited simulation study. In view of the results of the simulation study, it has been found that model based calibration estimator has outperformed the other estimators. However, calibration based regression estimator was found to be close to the model based calibration estimator.  

scholarly journals A Boundary Corrected Non-Parametric Regression Estimator for Finite Population Total

2019 ◽  
Vol 8 (3) ◽  
pp. 83
Author(s):  
Langat Reuben Cheruiyot ◽  
Odhiambo Romanus Otieno ◽  
George O. Orwa
Keyword(s):  
Boundary Problem ◽  
Nonparametric Regression ◽  
Finite Population ◽  
Simulated Data ◽  
The Other ◽  
Ratio Estimator ◽  
Regression Estimator ◽  
Population Total ◽  
Parametric Regression ◽  
Model Based

This study explores the estimation of finite population total. For many years design-based approach dominated the scene in statistical inference in sample surveys. The scenario has since changed with emergence of the other approaches (Model-Based, Model-Assisted and the Randomization-Assisted), which have proved to rival the conventional approach. This paper focuses on a model based approach. Within this framework a nonparametric regression estimator for finite population total is developed. The nonparametric technique has been found from previous studies to be advantageous than its parametric counterpart in terms of robustness and flexibility.  Kernel smoother has been used in construction of the estimator. The challenge of the boundary problem encountered with the Nadaraya-Watson estimator has been addressed by modifying it using reflection technique. The performance of the proposed estimator has been compared to the design-based Horvitz Thompson estimator and the model –based nonparametric regression estimator proposed by (Dorfman, 1992) and the ratio estimator using simulated data.

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