Ratio estimators using stratified random sampling and stratified ranked set sampling

2018 ◽  
Vol 8 (1) ◽  
pp. 85-89
Author(s):  
Monika Saini ◽  
Ashish Kumar
Keyword(s):  
Random Sampling ◽  
Ranked Set Sampling ◽  
Stratified Random Sampling ◽  
Ratio Estimators ◽  
Stratified Ranked Set Sampling

Stratified Ranked Set Sampling With Missing Observations for Estimating the Difference

2022 ◽  
pp. 209-232
Author(s):  
Carlos N. Bouza-Herrera
Keyword(s):  
Random Sampling ◽  
Real Data ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Missing Observations ◽  
Seminal Paper ◽  
Sample Error ◽  
The Difference ◽  
Stratified Ranked Set Sampling ◽  
Stratified Model

The authors develop the estimation of the difference of means of a pair of variables X and Y when we deal with missing observations. A seminal paper in this line is due to Bouza and Prabhu-Ajgaonkar when the sample and the subsamples are selected using simple random sampling. In this this chapter, the authors consider the use of ranked set-sampling for estimating the difference when we deal with a stratified population. The sample error is deduced. Numerical comparisons with the classic stratified model are developed using simulated and real data.

scholarly journals Ratio Estimators Using Coefficient of Variation and Coefficient of Correlation

2014 ◽  
Vol 8 (5) ◽  
pp. 70 ◽  
Author(s):  
Prayad Sangngam
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Stratified Random Sampling ◽  
Size Estimation ◽  
Auxiliary Variables ◽  
Coefficient Of Correlation ◽  
Ratio Estimators ◽  
Mean Square Errors

This paper introduces ratio estimators of the population mean using the coefficient of variation of  study variable and auxiliary variables together with the coefficient of correlation between the study and auxiliary variables under simple random sampling and stratified random sampling. These ratio estimators are almost unbiased. The mean square errors of the estimators and their estimators are given. Sample size estimation in both sampling designs are presented. An optimal sample size allocation in stratified random sampling is also suggested. Based on theoretical study, it can be shown that these ratio estimators have smaller MSE than the unbiased estimators. Moreover, the empirical study indicates that these ratio estimators have smallest MSE compared to the existing ones.

Ratio Estimators in Stratified Random Sampling

2003 ◽  
Vol 45 (2) ◽  
pp. 218-225 ◽  
Author(s):  
C. Kadilar ◽  
H. Cingi
Keyword(s):  
Random Sampling ◽  
Stratified Random Sampling ◽  
Ratio Estimators
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