scholarly journals Dexterous Estimation of Population Mean in Survey Sampling Under Non-Response Error

2019 ◽  
Vol 4 (6) ◽  
pp. 1307-1324
Author(s):  
S. K. Yadav ◽  
O. P. Yadav ◽  
D. K. Yadav
Keyword(s):  
Numerical Study ◽  
Survey Sampling ◽  
Efficient Estimation ◽  
Auxiliary Variables ◽  
Study Variable ◽  
Population Means ◽  
Population Mean ◽  
Highly Correlated ◽  
Primary Variable ◽  
Efficiency Conditions

In this scripture, we ponder the problem of efficient estimation of population mean of study variable utilizing information on highly correlated auxiliary variables under the presence of non-response on either of the variables. For this purpose, we suggest, an improved estimator under three different situations of non-response. Under the first situation, estimation of population mean is done with the problem of non-response on both the study and the auxiliary variables with the additional condition that the population means of the auxiliary variables are known. The second situation is to estimate the population mean of primary variable when the problem of non-response is only on the primary variable but the population means of the auxiliary variables are known while under the third situation estimation is performed with the problem of non-response on both the study and the auxiliary variables but population mean of one of the auxiliary variables is unknown. We study the sampling properties of the suggested estimator under above three different situations of non-response. We compare the proposed estimator with the competing estimators of population mean, under three different situations of non-response. The efficiency conditions are obtained for all three situations. A numerical study is also carried out to verify the efficiency conditions.

scholarly journals Generalized Chain Regression-cum-Chain Ratio Estimator for Population Mean under Stratified Extreme-cum-Median Ranked Set Sampling

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Asad Ali ◽  
Muhammad Moeen Butt ◽  
Muhammad Zubair
Keyword(s):  
Real Life ◽  
Auxiliary Information ◽  
Ranked Set Sampling ◽  
Efficient Estimation ◽  
Ratio Estimator ◽  
Auxiliary Variables ◽  
Study Variable ◽  
Data Set ◽  
Population Mean ◽  
Median Ranked Set Sampling

Estimation of population mean of study variable Y suffers loss of precision in the presence of high variation in the data set. The use of auxiliary information incorporated in construction of an estimator under ranked set sampling scheme results in efficient estimation of population mean. In this paper, we propose an efficient generalized chain regression-cum-chain ratio type estimator to estimate finite population mean of study variable under stratified extreme-cum-median ranked set sampling utilizing information on two auxiliary variables. Mean square error (MSE) of the proposed generalized estimator is derived up to first order of approximation. The applications of the proposed estimator under symmetrical and asymmetrical probability distributions are discussed using simulation study and real-life data set for comparisons of efficiency. It is concluded that the proposed generalized estimator performs efficiently as compared to some existing estimators. It is also observed that the efficiency of the proposed estimator is directly proportional to the correlations between the study variable and its auxiliary variables.

scholarly journals Estimation of Population Mean in Chain Ratio-Type Estimator under Systematic Sampling

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Mursala Khan ◽  
Rajesh Singh
Keyword(s):  
Finite Population ◽  
Real Data ◽  
Survey Sampling ◽  
Systematic Sampling ◽  
Auxiliary Variables ◽  
Data Set ◽  
Population Mean ◽  
First Order ◽  
The Mean ◽  
A Chain

A chain ratio-type estimator is proposed for the estimation of finite population mean under systematic sampling scheme using two auxiliary variables. The mean square error of the proposed estimator is derived up to the first order of approximation and is compared with other relevant existing estimators. To illustrate the performances of the different estimators in comparison with the usual simple estimator, we have taken a real data set from the literature of survey sampling.

scholarly journals Development of Efficient Estimation Technique for Population Mean in Two Phase Sampling Using Fuzzy Tools

2017 ◽  
Vol 13 (2) ◽  
pp. 5-28 ◽  
Author(s):  
P. Parichha ◽  
K. Basu ◽  
A. Bandyopadhyay ◽  
P. Mukhopadhyay
Keyword(s):  
Natural Population ◽  
Efficient Estimation ◽  
Double Sampling ◽  
Estimation Technique ◽  
Auxiliary Variables ◽  
Two Phase ◽  
Data Set ◽  
Population Mean ◽  
Phase Sampling ◽  
Two Phase Sampling

Abstract The present investigation deals with the problem of estimation of population mean in two-phase (double) sampling. Utilizing information on two auxiliary variables, one chain exponential ratio and regression type estimator has been proposed and its properties are studied under two different structures of twophase sampling. To make the estimator practicable, unbiased version of the proposed strategy has also been developed. The dominance of the suggested estimator over some contemporary estimators of population mean has been established through numerical illustrations carried over the data set of some natural population and artificially generated population. Categorization of the dominance ranges of the proposed estimation strategies are deployed through defuzzification tools, which are followed by suitable recommendations.

scholarly journals Almost Unbiased Ratio cum Product Estimator for Finite Population Mean with Known Median in Simple Random Sampling

2017 ◽  
Vol 1 ◽  
pp. 1-14
Author(s):  
Subramani Jambulingam ◽  
Ajith S. Master
Keyword(s):  
Random Sampling ◽  
Mean Squared Error ◽  
Numerical Study ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Study Variable ◽  
Population Mean ◽  
Squared Error ◽  
Product Estimator ◽  
Almost Unbiased

Introduction: In sampling theory, different procedures are used to obtain the efficient estimator of the population mean. The commonly used method is to obtain the estimator of the population mean is simple random sampling without replacement when there is no auxiliary variable is available. There are methods that use auxiliary information of the study characteristics. If the auxiliary variable is correlated with study variable, number of estimators are widely available in the literature.Objective: This study deals with a new ratio cum product estimator is developed for the estimation of population mean of the study variable with the known median of the auxiliary variable in simple random sampling.Materials and Methods: The bias and mean squared error of proposed estimator are derived and compared with that of the existing estimators by analytically and numerically.Results: The proposed estimator is less biased and mean squared error is less than that of the existing estimators and from the numerical study, under some known natural populations, the bias of proposed estimator is approximately zero and the mean squared error ranged from 6.83 to 66429.21 and percentage relative efficiencies ranged from 103.65 to 2858.75.Conclusion: The proposed estimator under optimum conditions is almost unbiased and performs better than all other existing estimators.Nepalese Journal of Statistics, 2017, Vol. 1, 1-14

scholarly journals Improved generalized family of estimators of population mean using information on transformed auxiliary variables

2018 ◽  
pp. 913-934 ◽  
Author(s):  
Housila P. Singh ◽  
Anita Yadav
Keyword(s):  
Mean Squared Error ◽  
Degree Of Approximation ◽  
Auxiliary Variables ◽  
Optimum Conditions ◽  
Study Variable ◽  
Population Mean ◽  
Mean Squared Errors ◽  
Squared Error

This paper addresses the problem of estimating the population mean  of the study variable using information on transformed auxiliary variables. In addition to many, Yasmeen et al (2015) estimator shown to the members of the suggested classes of estimators. We have derived the bias and mean squared error (MSE) of the suggested classes of estimators to the first degree of approximation. We have obtained the optimum conditions for which the suggested classes of estimators have minimum mean squared errors. It has been shown that the proposed classes of estimators are more efficient than the estimators recently envisaged by Yasmeen et al (2015) and other existing estimators.

scholarly journals A Class of Ratio-Type Estimator Using Two Auxiliary Variables for Estimating The Population Mean With Some Known Population Parameters

2019 ◽  
pp. 329-340 ◽  
Author(s):  
Toluwalase Janet Akingbade ◽  
Fabian C. Okafor
Keyword(s):  
Mean Square ◽  
Population Parameters ◽  
Auxiliary Variables ◽  
Study Variable ◽  
Sample Mean ◽  
Population Mean ◽  
The Mean ◽  
Classical Linear Regression ◽  
Linear Regression Estimator ◽  
Better Than

In this paper, we have suggested a class of ratio type estimators with a linear combination using two auxiliary variables with some known population mean of the study variable. The bias and the mean square error of the proposed estimators are derived. We identified sub-members of the class of ratio type estimators. The condition for which the the proposed the proposed estimators perform better than the sample mean per unit, Olkin (1958) multivariate ratio, classical linear regression estimator, Singh(1965), Mohanty (1967) and Swain (2012) are derived. From the analysis, it is observed that the proposed estimators perform better than the sample mean per unit and other existing ratio type estimators considered in this study.

scholarly journals Efficient Method of Estimating the Finite Population Mean Based on Two Auxiliary Variables in the Presence of Non-Response Under Stratified Sampling

2021 ◽  
Author(s):  
Housila P. Singh ◽  
Pragati Nigam
Keyword(s):  
Mean Squared Error ◽  
Auxiliary Variables ◽  
Optimum Conditions ◽  
Study Variable ◽  
Numerical Illustration ◽  
Population Mean ◽  
Minimum Mean Squared Error ◽  
Squared Error ◽  
Class Of Estimators ◽  
Better Than

This article addresses the problem of estimating the population mean using information on two auxiliary variables in presence of non-response on study variable only under stratified random sampling. A class of estimators has been defined. We have derived the bias and mean squared error up to first order of approximation. Optimum conditions are obtained in which the suggested class of estimators has minimum mean squared error. In addition to Chaudhury et al. (2009) estimator, many estimators can be identified as a member of the suggested class of estimators. It has been shown that the suggested class of estimators is better than the Chaudhury et al. (2009) estimator and other estimators. Results of the present study are supported through numerical illustration.

scholarly journals Estimation of Average Paddy Production of Pira Nagar Village at Barabanki District in India

2020 ◽  
Author(s):  
S. K. Yadav ◽  
Shanya Baghel ◽  
Sugandha Saxena ◽  
Avinash Kumar Singh
Keyword(s):  
Natural Population ◽  
Random Sampling ◽  
Uttar Pradesh ◽  
Simple Random Sampling ◽  
Sampling Scheme ◽  
Primary Data ◽  
Study Variable ◽  
Primary Variable ◽  
Efficiency Conditions ◽  
Better Than

The idea of the present paper is the use of the known information on study variable for enhanced estimation of average paddy production of Pira Nagar village at Barabanki District in India under the Simple Random Sampling Scheme. This known information is utilzed in the form of median of primary variable as it is readily available and does not require every unit of the population to be inquired. The Bias and MSE of the suggested estimator are derived up to approximation of degree one.The minimum value of the MSE of suggested estimator is also obtained by optimizing the characterizing scalar. The MSE has also been compared with the considered competing estimators both theoretically and empirically. The theoretical efficiency conditions of the suggested estimator to be better than the considered estimators are verified using natural population on primary data collected from Pira Nagar Village at Barabanki District of Uttar Pradesh state in India.

scholarly journals Bayesian Analysis of Finite Populations under Simple Random Sampling

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 318
Author(s):  
Manuel Mendoza ◽  
Alberto Contreras-Cristán ◽  
Eduardo Gutiérrez-Peña
Keyword(s):  
Bayesian Analysis ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Survey Sampling ◽  
Finite Populations ◽  
Population Means ◽  
Sampling Schemes ◽  
Nonparametric Approach ◽  
Conventional Procedure ◽  
Continuous And Discrete Variables

Statistical methods to produce inferences based on samples from finite populations have been available for at least 70 years. Topics such as Survey Sampling and Sampling Theory have become part of the mainstream of the statistical methodology. A wide variety of sampling schemes as well as estimators are now part of the statistical folklore. On the other hand, while the Bayesian approach is now a well-established paradigm with implications in almost every field of the statistical arena, there does not seem to exist a conventional procedure—able to deal with both continuous and discrete variables—that can be used as a kind of default for Bayesian survey sampling, even in the simple random sampling case. In this paper, the Bayesian analysis of samples from finite populations is discussed, its relationship with the notion of superpopulation is reviewed, and a nonparametric approach is proposed. Our proposal can produce inferences for population quantiles and similar quantities of interest in the same way as for population means and totals. Moreover, it can provide results relatively quickly, which may prove crucial in certain contexts such as the analysis of quick counts in electoral settings.

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