sampling without replacement
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2022 ◽  
pp. 26-41
Author(s):  
Beatriz Cobo ◽  
Elvira Pelle

In situations where the estimation of the proportion of sensitive variables relies on the observations of real measurements that are difficult to obtain, there is a need to combine indirect questioning techniques. In the present work, the authors will focus on the item count technique, with alternative methods of sampling, such as the ranked set sampling. They are based on the idea proposed by Santiago et al., which combines the randomized response technique proposed by Warner together with ranked set sampling. The authors will carry out a simulation study to compare the item count technique under ranked set sampling and under simple random sampling without replacement.



Author(s):  
Nitesh Kumar Adichwal ◽  
Abdullah Ali H. Ahmadini ◽  
Yashpal Singh Raghav ◽  
Rajesh Singh ◽  
Irfan Ali


Author(s):  
Holger Sambale ◽  
Arthur Sinulis

AbstractWe present concentration inequalities on the multislice which are based on (modified) log-Sobolev inequalities. This includes bounds for convex functions and multilinear polynomials. As an application, we show concentration results for the triangle count in the G(n, M) Erdős–Rényi model resembling known bounds in the G(n, p) case. Moreover, we give a proof of Talagrand’s convex distance inequality for the multislice. Interpreting the multislice in a sampling without replacement context, we furthermore present concentration results for n out of N sampling without replacement. Based on a bounded difference inequality involving the finite-sampling correction factor $$1 - (n / N)$$ 1 - ( n / N ) , we present an easy proof of Serfling’s inequality with a slightly worse factor in the exponent, as well as a sub-Gaussian right tail for the Kolmogorov distance between the empirical measure and the true distribution of the sample.





2021 ◽  
Vol 21 (2) ◽  
pp. 347-354
Author(s):  
MUHAMMAD IJAZ ◽  
TOLGA ZAMAN ◽  
BUSHRA HAIDER ◽  
SYED MUHAMMAD ASIM

The study suggests a class of product estimators for estimating the population mean of variable under investigation in simple random sampling without replacement (SRSWOR) scheme when secondary information on standard deviation, mean deviation, and quartile deviation is available. The expression for Bias and Mean Square Error (MSE) has been derived. A comparison is made both theoretically and numerically with other existing product estimators. It is concluded that compared to other product type estimators, suggested class of estimators estimate the population mean more efficiently.



2021 ◽  
Vol 11 (05) ◽  
pp. 854-869
Author(s):  
Nicholas Makumi ◽  
Romanus Odhiambo Otieno ◽  
George Otieno Orwa ◽  
Festus Were ◽  
Habineza Alexis


2020 ◽  
Vol 18 (2) ◽  
pp. 2-35
Author(s):  
Thomas R. Knapp

This primer is intended to provide the basic information for sampling without replacement from finite populations.



Author(s):  
J. O. Muili ◽  
E. N. Agwamba ◽  
Y. A. Erinola ◽  
M. A. Yunusa ◽  
A. Audu ◽  
...  

A percentile is one of the measures of location used by statisticians showing the value below which a given percentage of observations in a group of observations fall. A family of ratio-cum-product estimators for estimating the finite population mean of the study variable when the finite population mean of two auxiliary variables are known in simple random sampling without replacement (SRSWOR) have been proposed. The main purpose of this study is to develop new ratio-cum-product estimators in order to improve the precision of estimation of population mean in sample random sampling without replacement using information of percentiles with two auxiliary variables. The expressions of the bias and mean square error (MSE) of the proposed estimators were derived by Taylor series method up to first degree of approximation. The efficiency conditions under which the proposed ratio-cum-product estimators are better than sample man, ratio estimator, product estimator and other estimators considered in this study have been established. The numerical and empirical results show that the proposed estimators are more efficient than the sample mean, ratio estimator, product estimator and other existing estimators.



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