Accounting for dependent informative sampling in model-based finite population inference

Test ◽  
2020 ◽  
Author(s):  
Isabel Molina ◽  
Malay Ghosh
Keyword(s):  
Finite Population ◽  
Model Based ◽  
Population Inference ◽  
Informative Sampling

Design-based and model-based inference in survey sampling: appreciating the difference

1998 ◽  
Vol 28 (10) ◽  
pp. 1429-1447 ◽  
Author(s):  
T G Gregoire
Keyword(s):  
Finite Population ◽  
Survey Sampling ◽  
Finite Population Sampling ◽  
Population Sampling ◽  
Model Based ◽  
The Difference

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.

scholarly journals Estimation of the Number of Unemployed and Employed For Lithuanian Counties and Municipalities Using Models

2015 ◽  
Vol 54 (1) ◽  
pp. 33-44
Author(s):  
Linas Naujanis ◽  
Danutė Krapavickaitė
Keyword(s):  
Standard Deviation ◽  
Sampling Design ◽  
Finite Population ◽  
Labour Force ◽  
Parameters Estimation ◽  
Population Parameters ◽  
Model Based ◽  
Stein Estimator ◽  
Role Of Information

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.

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.

Potential outcomes and finite-population inference for M-estimators

2020 ◽  
Author(s):  
Ruonan Xu
Keyword(s):  
Finite Population ◽  
Recent Literature ◽  
Asymptotic Variance ◽  
General Setting ◽  
Ordinary Least Squares ◽  
Potential Outcomes ◽  
Inference Method ◽  
Population Inference ◽  
Sources Of Variation ◽  
The Common

Summary When a sample is drawn from or coincides with a finite population, the uncertainty of the coefficient estimators is often reported assuming the population is effectively infinite. The recent literature on finite-population inference instead derives an alternative asymptotic variance of the ordinary least squares estimator. Here, I extend the results to the more general setting of M-estimators and also find that the usual robust ‘sandwich’ estimator is conservative. The proposed asymptotic variance of M-estimators accounts for two sources of variation. In addition to the usual sampling-based uncertainty arising from (possibly) not observing the entire population, there is also design-based uncertainty, which is usually ignored in the common inference method, resulting from lack of knowledge of the counterfactuals. Under this alternative framework, we can obtain smaller standard errors of M-estimators when the population is treated as finite.

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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