Improved and Robust Estimators for Finite Population Variance Using Linear Combination of Probability Weighted Moment and Quartiles as Auxiliary Information

2018 ◽  
Vol 18 (4) ◽  
pp. 1-6
Author(s):  
M Bhat ◽  
S Maqbool ◽  
S Saraf ◽  
S Malik ◽  
Yasmeena Ismail ◽  
...  
Author(s):  
Uzma Yasmeen ◽  
Muhammad Noor-ul-Amin

The efficiency of the study variable can be improved by incorporating the information from the known auxiliary variables. Usually two techniques ratio and regression estimation are used with the help of auxiliary information in different approaches to acquire the high precision of the estimators. Considering the very heterogeneous population to get the size of the sample it may be originating impossible to get a sufficiently accurate and precise estimate by taking the simple random sampling technique from the complete population. Occasionally taking sample issue may differ significantly in different part of the entire population. For example, under study population consists of people living in apartments, own homes, hospitals and prisons or people living in plain regions and hill regions so in such situations the stratified sampling is one of the most commonly used approach to get a representative sample in survey sampling from different cross units of the population. The present study is set out on the recommendation of generalized variance estimators for finite population variance incorporating stratified sampling scheme with the information of single and two transformed auxiliary variables. The expressions of bias and mean square error (MSE) are obtained for the advised exponential type estimators. The conditions are obtained for which the anticipated estimators are better than the usual estimator. An empirical and simulation study is conducted to prove the superiority of the recommended estimator.


1998 ◽  
Vol 48 (3-4) ◽  
pp. 229-236 ◽  
Author(s):  
R.S. Biradar ◽  
H.P. Singh

Adopting predictive approach, estimators are proposed for population variance [Formula: see text] using different predictors for mean and variance of unobserved units in the population. Asymptotic expressions for bias and mean square error of these new estimators are obtained and compared with those of some known estimators of population variance. Predictive character of some known estimators is also examined . An empirical study demonstrated superiority over others of one of the proposed estimators, which uses regression estimators as predictors for mean and variance of unobserved units. Also, one of the other proposed estimators which does not utilize any auxiliary information has been found to be more efficient than the traditional unbiased estimator [Formula: see text].


2019 ◽  
Vol 17 (2) ◽  
Author(s):  
G. N. Singh ◽  
Mohd Khalid

In the case of sampling on two occasions, a class of estimators is considered which uses information on the first occasion as well as the second occasion in order to estimate the population means on the current (second) occasion. The usefulness of auxiliary information in enhancing the efficiency of this estimation is examined through the class of proposed estimators. Some properties of the class of estimators and a strategy of optimum replacement are discussed. The proposed class of estimators were empirically compared with the sample mean estimator in the case of no matching. The established optimum estimator, which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion, was empirically compared with the proposed class of estimators. Mutual comparisons of the proposed estimator were carried out. Suitable recommendations are made to the survey statistician for practical applications.


2013 ◽  
Vol 18 (3) ◽  
pp. 327-343 ◽  
Author(s):  
Dalius Pumputis ◽  
Andrius Čiginas

We consider the estimation of important parameters of a linear combination of order statistics (L-statistic) in a finite population, emphasizing the influence of auxiliary information on the estimation accuracy. Assuming that values of an auxiliary variable are available for all population units, we construct calibrated estimators for the variance of L-statistics and for the parameters, which define one-term Edgeworth expansions of distributions of L-statistics. The gain of the new estimators is demonstrated by the simulation study.


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