scholarly journals Ratio in Ratio Type Exponential Strategy for the Estimation of Population Mean

2021 ◽  
Author(s):  
Anurag Gupta ◽  
Rajesh Tailor
Keyword(s):  
Unbiased Estimator ◽  
Finite Population ◽  
Mean Squared Error ◽  
Double Sampling ◽  
Ratio Estimator ◽  
Population Mean ◽  
Squared Error ◽  
Double Sampling For Stratification

This paper is an attempt to develop an estimator for finite population mean. Motivated by Kiregyera (1984), a ratio in ratio type exponential strategy is developed for estimation of population mean in double sampling for stratification. To compare with relevant considered estimators, expressions for bias and mean squared error of the developed estimator have been derived. The developed estimator has been compared with usual unbiased estimator, Ige and Tripathi (1987), ratio estimator and ratio type exponential estimator given by Tailor et al (2014) theoretically as well as empirically.

scholarly journals A generalized exponential-type estimator for population mean using auxiliary attributes

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0246947
Author(s):  
Sohail Ahmad ◽  
Muhammad Arslan ◽  
Aamna Khan ◽  
Javid Shabbir
Keyword(s):  
Exponential Type ◽  
Random Sampling ◽  
Finite Population ◽  
Mean Squared Error ◽  
Simple Random Sampling ◽  
Stratified Random Sampling ◽  
Population Mean ◽  
First Order ◽  
Squared Error ◽  
Class Of Estimators

In this paper, we propose a generalized class of exponential type estimators for estimating the finite population mean using two auxiliary attributes under simple random sampling and stratified random sampling. The bias and mean squared error (MSE) of the proposed class of estimators are derived up to first order of approximation. Both empirical study and theoretical comparisons are discussed. Four populations are used to support the theoretical findings. It is observed that the proposed class of estimators perform better as compared to all other considered estimator in simple and stratified random sampling.

scholarly journals Efficient Estimator for Population Mean in Stratified Double Sampling in the Presence of Nonresponse Using One Auxiliary Variable

2021 ◽  
Vol 4 (2) ◽  
pp. 41-51
Author(s):  
A.E. Anieting ◽  
E. I. Enang ◽  
C. E. Onwukwe
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Variable ◽  
Double Sampling ◽  
Population Mean ◽  
Squared Error ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Second Phases ◽  
Optimal Values

A modified form of the population mean estimator suggested by Anieting and Enang (2020) in stratified double sampling in the presence of nonresponse using a single auxiliary variable has been proposed. The Mean Squared Error (MSE) and the bias of the proposed estimator have been given using large sample approximation. The empirical study shows that the MSE of the suggested estimator is more efficient than all other existing estimators in the same scheme. Determination of the optimal values of the first and second phases samples has also been done

scholarly journals A General Class of Dual to Ratio Estimator

2020 ◽  
pp. 421-431
Author(s):  
Housila Prasad Singh ◽  
Pragati Nigam
Keyword(s):  
General Class ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Ratio Estimator ◽  
Sample Surveys ◽  
Sample Mean ◽  
Population Mean ◽  
Squared Error ◽  
Class Of Estimators ◽  
Ratio Estimators

In this paper we have considered the problem of estimating the population mean using auxiliary information in sample surveys. A class of dual to ratio estimators has been defined. Exact expressions for bias and mean squared error of the suggested class of dual to ratio estimator have been obtained. In particular, properties of some members of the proposed class of dual to ratio estimators have been discussed. It has been shown that the proposed class of estimators is more efficient than the sample mean, ratio estimator, dual to ratio estimator and some members of the suggested class of estimators in some realistic conditions. Some numerical illustrations are given in support of the present study.

scholarly journals Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Nelson Kiprono Bii ◽  
Christopher Ouma Onyango ◽  
John Odhiambo
Keyword(s):  
Finite Population ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Cluster Sampling ◽  
Sample Surveys ◽  
Population Mean ◽  
Second Stage ◽  
Kernel Weights ◽  
Squared Error ◽  
Interval Lengths

Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys.

scholarly journals Chain Ratio Type Estimators Using Known Parameters of Auxiliary Variates in Double Sampling

2020 ◽  
Author(s):  
Priya Mehta ◽  
Rajesh Tailor
Keyword(s):  
Empirical Study ◽  
Unbiased Estimator ◽  
Mean Squared Error ◽  
Population Data ◽  
Double Sampling ◽  
Numerical Illustration ◽  
Data Set ◽  
Squared Error ◽  
Large Sample Approximation ◽  
Sample Approximation

This paper discusses chain ratio type estimator for estimation of population mean in double sampling. The developed estimator uses two auxiliary variates associated with study variate in order to increases its efficiency. The developed estimator has been compared with usual unbiased estimator and other existing estimators. The expression for the bias and mean squared error of the developed estimator is obtained under large sample approximation. We have considered the natural population data set to examine the merits of the developed estimator and carried out the empirical study in support of theoretical findings. Numerical illustration shows that the proposed estimator is more efficient.

scholarly journals Two- Phase Stratified Sampling Estimator for Population Mean in The Presence of Nonresponse Using Single Auxiliary Variable

2020 ◽  
Vol 8 (2) ◽  
pp. 49-56
Author(s):  
Akan Anieting
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Variable ◽  
Stratified Sampling ◽  
Second Phase ◽  
Two Phase ◽  
Population Mean ◽  
Squared Error ◽  
Large Sample Approximation ◽  
Sample Approximation ◽  
Phase Sample

In this article, a new estimator for population mean in two-phase stratified sampling in the presence of nonresponse using single auxiliary variable has been proposed. The bias and Mean Squared Error (MSE) of the proposed estimator has been given using large sample approximation. The empirical study shows that the MSE of the proposed estimator is more efficient than existing estimators. The optimum values of first and second phase sample have been determined.

scholarly journals A Family of Difference-Cum-Exponential Type Estimators for Estimating the Population Variance Using Auxiliary Information in Sample Surveys

2014 ◽  
Vol 1 ◽  
pp. 15-21
Author(s):  
H.S. Jhajj ◽  
Kusam Lata
Keyword(s):  
Linear Regression ◽  
Exponential Type ◽  
Random Sampling ◽  
Sampling Design ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Double Sampling ◽  
Population Variance ◽  
Squared Error

Using auxiliary information, a family of difference-cum-exponential type estimators for estimating the population variance of variable under study have been proposed under double sampling design. Expressions for bias, mean squared error and its minimum values have been obtained. The comparisons have been made with the regression-type estimator by using simple random sampling at both occasions in double sampling design. It has also been shown that better estimators can be obtained from the proposed family of estimators which are more efficient than the linear regression type estimator. Results have also been illustrated numerically as well asgraphically.

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