Quantile interval estimation in finite population using a multivariate ratio estimator

Metrika ◽  
1998 ◽  
Vol 47 (1) ◽  
pp. 203-213 ◽  
Author(s):  
M. Rueda García ◽  
A. Arcos Cebrián ◽  
E. Artés Rodríguez
Keyword(s):  
Finite Population ◽  
Interval Estimation ◽  
Ratio Estimator

On the Admissibility of the Symmetrized Des Raj Estimator for PPSWOR Samples of Size Two

1980 ◽  
Vol 29 (1-2) ◽  
pp. 35-44 ◽  
Author(s):  
S. Sengupta
Keyword(s):  
Population Size ◽  
Sampling Design ◽  
Finite Population ◽  
Ratio Estimator ◽  
Population Total ◽  
Sample Mean ◽  
Unbiased Estimators ◽  
Linear Estimators

The symmetrized Des Raj estimator for a finite population total based on a PPSWOR sample of size two is shown to be admissible within (i) the class of all linear estimators and (ii) the class of all unbiased estimators. In this connection we have obtained a class of admissible linear estimators of the population total which includes the sample mean multiplied by the population size and the classical ratio estimator for any arbitrary sampling design.

scholarly journals A Comparison of Some Approximate Confidence Intervals for a Single Proportion for Clustered Binary Outcome Data

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Krishna K. Saha ◽  
Daniel Miller ◽  
Suojin Wang
Keyword(s):  
Biomedical Applications ◽  
Profile Likelihood ◽  
Interval Estimation ◽  
Estimating Equation ◽  
Outcome Data ◽  
Biomedical Data ◽  
Ratio Estimator ◽  
Binary Outcome ◽  
Complex Survey ◽  
Complex Survey Data

AbstractInterval estimation of the proportion parameter in the analysis of binary outcome data arising in cluster studies is often an important problem in many biomedical applications. In this paper, we propose two approaches based on the profile likelihood and Wilson score. We compare them with two existing methods recommended for complex survey data and some other methods that are simple extensions of well-known methods such as the likelihood, the generalized estimating equation of Zeger and Liang and the ratio estimator approach of Rao and Scott. An extensive simulation study is conducted for a variety of parameter combinations for the purposes of evaluating and comparing the performance of these methods in terms of coverage and expected lengths. Applications to biomedical data are used to illustrate the proposed methods.

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.

scholarly journals Enhanced Estimators of Population Variance with the Use of Supplementary Information in Survey Sampling

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Showkat Ahmad Lone ◽  
Mir Subzar ◽  
Ankita Sharma
Keyword(s):  
Finite Population ◽  
Auxiliary Variable ◽  
Supplementary Information ◽  
Ratio Estimator ◽  
Population Variance ◽  
Measures Of Dispersion ◽  
Class Of Estimators ◽  
Efficiency Comparison ◽  
Improved Estimators ◽  
Theoretical Results

In the present study, we propose the proficient class of estimators of the finite population mean, while incorporating the nonconventional location and nonconventional measures of dispersion with coefficient of variation of the auxiliary variable. Properties associated with the suggested class of improved estimators are derived, and an efficiency comparison with the usual unbiased ratio estimator and other existing estimators under consideration in the present study is established. An empirical study has also been provided to validate the theoretical results. Finally, it is established that the proposed class of estimators of the finite population variance proves to be more efficient than the existing estimators mentioned in this study.

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