The Efficiency of a Ratio Product Estimator in the Estimation of the Finite Population Coefficient of Variation (original) (raw)

Logarithmic-Product-Cum-Ratio Type Estimator for Estimating Finite Population Coefficient of Variation

Oriental Journal of Physical Sciences, 2022

Estimation of population parameters have been a challenging aspect in sample survey for sometimes and many efforts have been made to enhance the exactness of the parameters of these estimators. We suggested the logarithmic-product-cum-ratio type estimator. Expression of the MSE of the intended estimator originated using the Taylor series technique. A numerical illustration was conducted and the results revealed that the modified work is ameliorated than sample means with other estimators observed.

Logarithmic Ratio-Type Estimator of Population Coefficient of Variation

Asian Journal of Probability and Statistics

The estimation of population coefficient of variation is one of the challenging aspects in sampling survey techniques for the past decades and much effort has been employed to develop estimators to produce its efficient estimate. In this paper, we proposed logarithmic ratio type estimator for the estimating population coefficient of variation using logarithm transformation on the both population and sample variances of the auxiliary character. The expression for the mean squared error (MSE) of the proposed estimator has been derived using Taylor series first order approximation approach. Efficiency conditions of the proposed estimator over other estimators in the study has also been derived. The empirical study was conducted using two-sets of populations and the results showed that the proposed estimator is more efficient. This result implies that, the estimate of proposed estimator will be closer to the true parameter than the estimates of other estimators in the study.

A modified ratio-product estimator of population mean using some known parameters of the auxiliary variable

Bayero Journal of Pure and Applied Sciences, 2017

The estimation of population mean is one of the challenging aspects in sampling theory and population study and much effort has been vigorously employed to improve the precision of estimates. In this research work, a modified rati study variable Y using median and coefficient of variation of the auxiliary variable random sampling scheme is proposed. T have been obtained under large sample approximation, asymptotically optimum estimator (AOE) is identified with its approximate MSE formula. Estimator based on "estimated optimum values" was also investigated. Theoretical and empirical comparison of proposed estimator with some other ratio and product estimators justified the performance of the proposed estimators. A minimum of 20 percent reduction in the MSE were observed from each of the existing esti is found that the proposed estimator were uniformly better than all other modified ratio and product estimators and thus most preferred over the existing estimators for the use in practical application.

On the Efficiency of Some Modified Ratio and Product Estimators – The Optimal C x Approach

In this paper, an optimal estimator for estimating the population mean is proposed. This is ach ieved by minimizing the coefficient of variation (C x) of the au xiliary variable in the Mean Square Error (MSE) fro m the existing estimators. Using well analyzed data to illustrate the procedure for both the Ratio and Product estimators, a min imu m of 10 percent reduction in the MSE were observed from each of the existing estimators considered. The proposed optimal estimators is unifo rmly better than all other estimators and thus most preferred over the existing modified ratio and product estimators for the use in practical applicat ions for certain population with peculiar characteristics

Difference-Cum-Ratio Estimators for Estimating Finite Population Coefficient of Variation in Simple Random Sampling

Asian Journal of Probability and Statistics

In this paper, three difference-cum-ratio estimators for estimating finite population coefficient of variation of the study variable using known population mean, population variance and population coefficient of variation of auxiliary variable were suggested. The biases and mean square errors (MSEs) of the proposed estimators were obtained. The relative performance of the proposed estimators with respect to that of some existing estimators were assessed using two populations’ information. The results showed that the proposed estimators were more efficient than the usual unbiased, ratio type, exponential ratio-type, difference-type and other existing estimators considered in the study.

Ratio Estimators Using Coefficient of Variation and Coefficient of Correlation

Modern Applied Science, 2014

This paper introduces ratio estimators of the population mean using the coefficient of variation of study variable and auxiliary variables together with the coefficient of correlation between the study and auxiliary variables under simple random sampling and stratified random sampling. These ratio estimators are almost unbiased. The mean square errors of the estimators and their estimators are given. Sample size estimation in both sampling designs are presented. An optimal sample size allocation in stratified random sampling is also suggested. Based on theoretical study, it can be shown that these ratio estimators have smaller MSE than the unbiased estimators. Moreover, the empirical study indicates that these ratio estimators have smallest MSE compared to the existing ones.

Some Improved Class of Ratio Estimators for Finite Population Variance with the Use of Known Parameters

CERN European Organization for Nuclear Research - Zenodo, 2022

In this paper, we proposed some improved class of ratio estimators for finite population variance with the use of known parameters. The proposed estimators are obtained by transforming both the sample variances of the study and auxiliary variables, as well as the use of known parameters. The Mean Square Error of the proposed estimators have been obtained and the conditions for their efficiency over some existing variance estimators have been established. The present family of finite variance estimator, having obtaining the optimal values of the constants, exhibit significant improvement over the estimators considered in the study. The empirical study is also conducted to support the theoretical results and the results revealed that the suggested estimators are more efficient.

Ratio-Cum-Product Estimators of Population Mean Using Known Population Parameters of Auxiliary Variates

Communications for Statistical Applications and Methods, 2011

This paper suggests two ratio-cum-product estimators of finite population mean using known coefficient of variation and coefficient of kurtosis of auxiliary characters. The bias and mean squared error of the proposed estimators with large sample approximation are derived. It has been shown that the estimators suggested by Upadhyaya and Singh (1999) are particular case of the suggested estimators. Almost ratio-cum product estimators of suggested estimators have also been obtained using Jackknife technique given by Quenouille (1956). An empirical study is also carried out to demonstrate the performance of the suggested estimators.

An Improved Estimator of Population Variance using known Coefficient of Variation

In the present article, an improved estimator (s 2 y k) over usual unbiased estimator of population variance (s 2 y) is proposed by using known coefficient of variation (C y) of the study variable y. Asymptotic expression for its bias and mean square error (MSE) have been obtained. For more practical utility the study of proposed estimator under estimated optimum value of k has also been carried out. A comparative study has been made between the proposed estimator and the conventional estimator. Numerical illustration is also given in support of the present study.