scholarly journals A Generalized Class of Exponential Type Estimators for Population Mean under Systematic Sampling Using Two Auxiliary Variables

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Mursala Khan
Keyword(s):  
Exponential Type ◽  
Secondary Data ◽  
Estimation Procedure ◽  
The Other ◽  
Systematic Sampling ◽  
Auxiliary Variables ◽  
Population Mean ◽  
First Order ◽  
Class Of Estimators ◽  
The Mean

We have proposed a generalized class of exponential type estimators for population mean under the framework of systematic sampling using the knowledge of two auxiliary variables. The expressions for the mean square error of the proposed class of estimators have been corrected up to first order of approximation. Comparisons of the efficiency of the proposed class of estimators under the optimal conditions with the other existing estimators have been presented through a real secondary data. The statistical study provides strong evidence that the proposed class of estimators in survey estimation procedure results in substantial efficiency improvements over the other existing estimation approaches.

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 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 Measuring Performance of Ratio-Exponential-Log Type General Class of Estimators Using Two Auxiliary Variables

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Javid Shabbir ◽  
Shakeel Ahmed ◽  
Aamir Sanaullah ◽  
Ronald Onyango
Keyword(s):  
General Class ◽  
Finite Population ◽  
Real Life ◽  
Auxiliary Variables ◽  
First Order ◽  
Class Of Estimators ◽  
The Mean ◽  
Ratio Estimators ◽  
Efficiency Comparison ◽  
Log Ratio

In this paper, a ratio-exponential-log type general class of estimators is proposed in estimating the finite population mean using two auxiliary variables when population parameters of the auxiliary variables are known. From the proposed estimator, some special estimators are identified as members of the proposed general class of estimators. The mean square error (MSE) expressions are obtained up to the first order of approximation. This study finds that the proposed general class of estimators outperforms as compared to the conventional mean estimator, usual ratio estimators, exponential-ratio estimators, log-ratio type estimators, and many other competitor regression type estimators. Four real-life applications are used for efficiency comparison.

scholarly journals A general class of estimators on estimating population mean using the auxiliary proportions under simple and two phase sampling

2021 ◽  
Vol 6 (12) ◽  
pp. 13592-13607
Author(s):  
Xuechen Liu ◽  
◽  
Muhammad Arslan ◽  
Keyword(s):  
Finite Population ◽  
Two Phase ◽  
Population Mean ◽  
Mean Squared Errors ◽  
First Order ◽  
Mathematical Expressions ◽  
Phase Sampling ◽  
Class Of Estimators ◽  
The Mean ◽  
Two Phase Sampling

<abstract><p>This article deals with estimation of finite population mean using the auxiliary proportion under simple and two phase sampling scheme utilizing two auxiliary variables. Mathematical expressions for the mean squared errors of the proposed estimators are derived under first order of approximation. We compare the proposed class of estimators "theoretically and numerically" with the usual mean estimator of Naik and Gupta <sup>[<xref ref-type="bibr" rid="b1">1</xref>]</sup>. The theoretical as well as numerical findings support the superiority of our proposed class of estimator as compared to estimators available in literature.</p></abstract>

scholarly journals An Improved Exponential Type Estimator Of Population Mean of Sensitive Variable Using Optional Randomized Response Technique

2019 ◽  
pp. 49-59
Author(s):  
Lovleen Kumar Grover ◽  
Amanpreet Kaur
Keyword(s):  
Mean Square Error ◽  
Exponential Type ◽  
Mean Square ◽  
Order Of Approximation ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Population Mean ◽  
First Order ◽  
The Mean ◽  
Sensitive Variable

In this paper, we improve the efficiency of  Koyuncu et al (2014)’s estimator of population mean of sensitive variable by replacing Traditional Randomized response technique with Optional Randomized response technique as suggested by Gupta et al (2014). The mean square error of proposed estimator is obtained, up to first order of approximation, and is compared with mean square error of various existing estimators theoretically as well as numerically.

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.

Moisture estimation within a mine heap: An application of cokriging with assay data and electrical resistivity

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. B11-B23 ◽  
Author(s):  
Dale Rucker
Keyword(s):  
Electrical Resistivity ◽  
Secondary Data ◽  
Estimation Procedure ◽  
Primary Data ◽  
Low Grade ◽  
Least Squares Regression ◽  
Data Set ◽  
Correlation Scale ◽  
The Mean ◽  
Moisture Estimation

Cokriging has been applied to estimate the distribution of moisture within a rock pile of low-grade gold ore, or heap. Along with the primary data set of gravimetric moisture content obtained from drilling, electrical resistivity was used to supplement the estimation procedure by supplying a secondary data set. The effectiveness of the cokriging method was determined by comparing the results to kriging the moisture data alone and through least-squares regression (LSR) modeling of colocated resistivity and moisture. In general, the wells from which moisture data were derived were separated by distances far greater than the horizontal correlation scale. The kriging results showed that regions generally undersampled by drilling reverted to the mean of the moisture data. The LSR technique, which provides a simpletransformation of resistivity to moisture, converted the low resis-tivity to highmoisture, and vice versa. The sparse well locations created a high degree of uncertainty in the transformed data set. Extreme resistivity values produced nonphysical moisture values, either negative for the linear model or values greater than one for the power model. The cokriging application, which considers the correlation scale and secondary data, produced the best results, as indicated through the cross validation. The mean and variance of the cokriged moisture were closer to the measured moisture, and the bias in the residuals was the lowest. The application likely could be improved through optimal well placement, whereby the resistivity results guide the drilling program through gross target characterization, and the moisture estimation could be updated iteratively.

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 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.

Sign in / Sign up

Export Citation Format

Share Document