Predictive Estimation of Finite Population Variance

1998 ◽  
Vol 48 (3-4) ◽  
pp. 229-236 ◽  
Author(s):  
R.S. Biradar ◽  
H.P. Singh
Keyword(s):  
Finite Population ◽  
Auxiliary Information ◽  
The Other ◽  
Mean Square ◽  
Population Variance ◽  
Asymptotic Expressions ◽  
Regression Estimators ◽  
Predictive Approach ◽  
Variance Formula ◽  
Mean And Variance

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].

scholarly journals An Alternative Estimator for Estimating the Finite Population Mean Using Auxiliary Information in Sample Surveys

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ramkrishna S. Solanki ◽  
Housila P. Singh ◽  
Anjana Rathour
Keyword(s):  
Finite Population ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Population Mean ◽  
Asymptotic Expressions ◽  
Variance Formula ◽  
Optimum Estimator ◽  
Class Of Estimators ◽  
Alternative Estimator ◽  
Usual Product

This paper suggests a class of estimators for estimating the finite population mean of the study variable using known population mean of the auxiliary variable . Asymptotic expressions of bias and variance of the suggested class of estimators have been obtained. Asymptotic optimum estimator (AOE) in the class is identified along with its variance formula. It has been shown that the proposed class of estimators is more efficient than usual unbiased, usual ratio, usual product, Bahl and Tuteja (1991), and Kadilar and Cingi (2003) estimators under some realistic conditions. An empirical study is carried out to judge the merits of suggested estimator over other competitors practically.

scholarly journals A Study on the Chain Ratio-Type Estimator of Finite Population Variance

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yunusa Olufadi ◽  
Cem Kadilar
Keyword(s):  
Finite Population ◽  
Numerical Comparison ◽  
Mean Square ◽  
Auxiliary Variables ◽  
Population Variance ◽  
Two Phase ◽  
First Order ◽  
Phase Sampling ◽  
The Mean ◽  
Two Phase Sampling

We suggest an estimator using two auxiliary variables for the estimation of the unknown population variance. The bias and the mean square error of the proposed estimator are obtained to the first order of approximations. In addition, the problem is extended to two-phase sampling scheme. After theoretical comparisons, as an illustration, a numerical comparison is carried out to examine the performance of the suggested estimator with several estimators.

scholarly journals The Estimation of Finite Population Variance Under Stratified Sampling Technique

2021 ◽  
Author(s):  
Uzma Yasmeen ◽  
Muhammad Noor-ul-Amin
Keyword(s):  
Finite Population ◽  
Auxiliary Information ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Auxiliary Variables ◽  
Population Variance ◽  
Variance Estimators ◽  
Usual Estimator ◽  
Study Population

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.

scholarly journals Development of Estimation Procedure of Population Variance in Stratified Randomized Response Technique

2020 ◽  
pp. 1-8
Author(s):  
Nadia Mushtaq ◽  
Iram Saleem
Keyword(s):  
Variance Estimation ◽  
Auxiliary Information ◽  
Estimation Procedure ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Population Variance ◽  
Regression Estimators ◽  
Class Of Estimators ◽  
Estimation Of Variance ◽  
Sensitive Variable

Singh et al. (2016) presented a ratio and regression estimators of population variance of a sensitive variable using auxiliary information based on randomized response technique (RRT). In this article, the RRT is considered in stratified random sampling for the estimation of variance. A generalized class of estimators of variance in stratified RRT is proposed and derive the procedure of variance estimation in stratified RRT. The expression of the bias and mean square error are expressed. The empirical findings support the soundness of proposed scheme of variance estimation.

scholarly journals Some Improved Ratio, Product, and Regression Estimators of Finite Population Mean When Using Minimum and Maximum Values

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Manzoor Khan ◽  
Javid Shabbir
Keyword(s):  
Finite Population ◽  
Auxiliary Information ◽  
Efficient Estimation ◽  
Order Of Approximation ◽  
Population Mean ◽  
Mean Squared Errors ◽  
First Order ◽  
Regression Estimators ◽  
Improved Performance

Efficient estimation of finite population mean is carried out by using the auxiliary information meaningfully. In this paper we have suggested some modified ratio, product, and regression type estimators when using minimum and maximum values. Expressions for biases and mean squared errors of the suggested estimators have been derived up to the first order of approximation. The performances of the suggested estimators, relative to their usual counterparts, have been studied, and improved performance has been established. The improvement in efficiency by making use of maximum and minimum values has been verified numerically.

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