scholarly journals Some Median Type Estimators to Estimate the Finite Population Mean

2020 ◽  
pp. 48-58
Author(s):  
Waqar Hafeez ◽  
Javid Shabbir ◽  
Muhammad Taqi Shah ◽  
Shakeel Ahmed
Keyword(s):  
Linear Regression ◽  
Finite Population ◽  
Auxiliary Variable ◽  
Maximum Efficiency ◽  
Order Of Approximation ◽  
Study Variable ◽  
Sample Mean ◽  
First Order ◽  
Regression Estimators ◽  
Class Of Estimators

Researchers always appreciates estimators of finite population quantities, especially mean, with maximum efficiency for reaching to valid statistical inference.  Apart from ratio, product and regression estimators, exponential estimators are widely considered by survey statisticians. Motivated from the idea of exponential type estimators, in this article, we propose some new estimators utilizing known median of the study variable with mean of auxiliary variable. Theoretical properties of the suggested estimators are studied up to first order of approximation. In addition, an empirical and simulation study the comparison of median based proposed class of estimators with sample mean, ratio and linear regression estimators  are discussed. The results expose that the proposed estimators are more efficient than the existing estimators.

scholarly journals Restructured class of estimators for population mean using an auxiliary variable under simple random sampling scheme

2020 ◽  
Vol 16 (1) ◽  
pp. 61-75
Author(s):  
S. Baghel ◽  
S. K. Yadav
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Sampling Scheme ◽  
Order Of Approximation ◽  
Study Variable ◽  
Population Mean ◽  
First Order ◽  
New Class ◽  
Class Of Estimators

AbstractThe present paper provides a remedy for improved estimation of population mean of a study variable, using the information related to an auxiliary variable in the situations under Simple Random Sampling Scheme. We suggest a new class of estimators of population mean and the Bias and MSE of the class are derived upto the first order of approximation. The least value of the MSE for the suggested class of estimators is also obtained for the optimum value of the characterizing scaler. The MSE has also been compared with the considered existing competing estimators both theoretically and empirically. The theoretical conditions for the increased efficiency of the proposed class, compared to the competing estimators, is verified using a natural population.

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.

scholarly journals Improved Estimator of Finite Population Variance Using Coefficient of Quartile Deviation

2018 ◽  
pp. 1-6
Author(s):  
Komal Javed ◽  
Nasir Jamal ◽  
Muhammad Hanif ◽  
Muhammad Ali ◽  
Usman Shahzad ◽  
...  
Keyword(s):  
Finite Population ◽  
Secondary Data ◽  
Auxiliary Variable ◽  
Data Sets ◽  
Population Variance ◽  
Study Variable ◽  
Variance Estimators ◽  
Class Of Estimators ◽  
Efficiency Comparison ◽  
Variable Bias

This study introduces a new, better, class of ratio estimators for the estimation of population variance of the study variable by using the coefficient of quartile deviation of auxiliary variable. Bias and mean square error of the proposed class of estimators are also derived. The conditions of efficiency comparison are also obtained. Simulation and different secondary data sets are used to evaluate the efficiency of proposed class of variance estimators over existing class of estimators. The empirical study shows that the suggested class of estimators is more efficient the existing class of estimators for the population variance.

A Class of Estimators of the Population Mean Using Multi­Auxiliary Information

1983 ◽  
Vol 32 (1-2) ◽  
pp. 47-56 ◽  
Author(s):  
S. K. Srivastava ◽  
H. S. Jhajj
Keyword(s):  
Mean Squared Error ◽  
Broad Class ◽  
Auxiliary Variable ◽  
Population Parameters ◽  
Auxiliary Variables ◽  
Sample Mean ◽  
Squared Error ◽  
Class Of Estimators ◽  
The Mean ◽  
Asymptotic Mean Squared Error

For estimating the mean of a finite population, Srivastava and Jhajj (1981) defined a broad class of estimators which we information of the sample mean as well as the sample variance of an auxiliary variable. In this paper we extend this class of estimators to the case when such information on p(> 1) auxiliary variables is available. The estimators of the class involve unknown constants whose optimum values depend on unknown population parameters. When these population parameters are replaced by their consistent estimates, the resulting estimators are shown to have the same asymptotic mean squared error. An expression by which the mean squared error of such estimators is smaller than those which use only the population means of the auxiliary variables, is obtained.

scholarly journals Estimation of finite population distribution function with dual use of auxiliary information under non-response

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243584
Author(s):  
Sardar Hussain ◽  
Sohaib Ahmad ◽  
Sohail Akhtar ◽  
Amara Javed ◽  
Uzma Yasmeen
Keyword(s):  
Distribution Function ◽  
Finite Population ◽  
Population Distribution ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Distribution Functions ◽  
Auxiliary Variables ◽  
Sample Mean ◽  
Sample Distribution

In this paper, we propose two new families of estimators for estimating the finite population distribution function in the presence of non-response under simple random sampling. The proposed estimators require information on the sample distribution functions of the study and auxiliary variables, and additional information on either sample mean or ranks of the auxiliary variable. We considered two situations of non-response (i) non-response on both study and auxiliary variables, (ii) non-response occurs only on the study variable. The performance of the proposed estimators are compared with the existing estimators available in the literature, both theoretically and numerically. It is also observed that proposed estimators are more precise than the adapted distribution function estimators in terms of the percentage relative efficiency.

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 Comparison of Various Methods for Estimating Finite Population Total in Survey Sampling When Study Variable and Auxiliary Variable are Inversely Related

2019 ◽  
Vol 11 (1) ◽  
pp. 15-22
Author(s):  
S. Kumar ◽  
B. V. S. Sisodia
Keyword(s):  
Simulation Study ◽  
Finite Population ◽  
Auxiliary Variable ◽  
Survey Sampling ◽  
The Other ◽  
Regression Estimator ◽  
Study Variable ◽  
Population Total ◽  
Model Based ◽  
Proposed Model

In the present paper, a model based calibration estimator of population total has been developed when study variable y and auxiliary variable x are inversely related. The relative performance of the proposed model based calibration estimator in comparison to model based estimator, the usual regression estimator and calibration based regression estimator have been examined by conducting a limited simulation study. In view of the results of the simulation study, it has been found that model based calibration estimator has outperformed the other estimators. However, calibration based regression estimator was found to be close to the model based calibration estimator.  

scholarly journals A Note on Generalized Exponential Type Estimator for Population Variance in Survey Sampling

2015 ◽  
Vol 38 (2) ◽  
pp. 385-397 ◽  
Author(s):  
Javid Shabbir ◽  
Sat Gupta
Keyword(s):  
Exponential Type ◽  
Auxiliary Variable ◽  
Survey Sampling ◽  
Data Sets ◽  
Numerical Comparison ◽  
Order Of Approximation ◽  
Population Variance ◽  
First Order ◽  
Better Than

<p>Recently a new generalized estimator for population variance using information on the auxiliary variable has been introduced by Asghar, Sanaullah &amp; Hanif (2014). In that paper there was some inaccuracy in the bias and MSE expressions. In this paper, we provide the correct expressions for bias and MSE of the Asghar et al. (2014) estimator, up to the first order of approximation. We also propose a new generalized exponential type estimator for population variance which performs better than the existing estimators. Four data sets are used for numerical comparison of efficiencies.</p>

scholarly journals Ameliorated Class of Estimators of Finite Population Variance

2020 ◽  
pp. 1-14
Author(s):  
J. O. Muili ◽  
E. N. Agwamba ◽  
A. B. Odeyale ◽  
A. Adebiyi
Keyword(s):  
Finite Population ◽  
Population Variance ◽  
First Order ◽  
Class Of Estimators ◽  
The Mean ◽  
Ratio Estimators ◽  
Mean Square Errors ◽  
Theoretical Results ◽  
Percentage Relative Efficiency ◽  
Taylor’S Series

A class of ratio estimators of finite population variance is proposed in this study. The properties of the proposed estimators have been derived using Taylor’s Series method up to first order of approximation. The efficiency conditions which are the mean square errors (MSEs) and percentage relative efficiency (PRE) of the proposed estimators over existing estimators have been established. The analytical illustration was also conducted to affirm the theoretical results. The results of the empirical study revealed that the proposed estimators are more efficient than the existing estimators considered in the study.

scholarly journals Unbiased Estimation of Population Mean Using Lahiri-Midzuno-Sen Type Sampling Scheme

2020 ◽  
pp. 16-20
Author(s):  
Chandni Kumari ◽  
Ratan Kumar Thakur
Keyword(s):  
Random Sampling ◽  
Sampling Strategy ◽  
Simple Random Sampling ◽  
Sampling Scheme ◽  
Unbiased Estimation ◽  
Double Sampling ◽  
Order Of Approximation ◽  
Population Mean ◽  
First Order ◽  
Class Of Estimators

This paper considers the problem of estimating the population mean under double sampling. We have suggested the generalized class of estimators under Lahiri (1951) to Midzuno (1952) and Sen (1952) type sampling scheme and its properties are studied up to the first order of approximation. Further, we compare the proposed sampling strategy with some conventional estimators under the simple random sampling without replacement. On the basis of suitable range information, we give some concluding remarks related to propose sampling strategy. An empirical study is given in support of the present study.

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