Notice of Retraction: Some new ratio estimators using coefficient of variation and Kurtosis of auxiliary variate

2010 ◽  
Author(s):  
Jingli Lu ◽  
Zaizai Yan ◽  
Changjiang Ding ◽  
Zhimin Hong
Keyword(s):  
Coefficient Of Variation ◽  
Auxiliary Variate ◽  
Ratio Estimators

scholarly journals Ratio Estimators Using Coefficient of Variation and Coefficient of Correlation

2014 ◽  
Vol 8 (5) ◽  
pp. 70 ◽  
Author(s):  
Prayad Sangngam
Keyword(s):  
Sample Size ◽  
Coefficient Of Variation ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Stratified Random Sampling ◽  
Size Estimation ◽  
Auxiliary Variables ◽  
Coefficient Of Correlation ◽  
Ratio Estimators ◽  
Mean Square Errors

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.

scholarly journals Enhancing the Mean Ratio Estimators for Estimating Population Mean Using Non-Conventional Location Parameters

2016 ◽  
Vol 39 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Muhammad Abid ◽  
Nasir Abbas ◽  
Hafiz Zafar Nazir ◽  
Zhengyan Lin
Keyword(s):  
Empirical Study ◽  
Coefficient Of Variation ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Linear Combinations ◽  
Population Correlation ◽  
Location Parameters ◽  
The Mean ◽  
Ratio Estimators ◽  
Mean Square Errors

<p>Conventional measures of location are commonly used to develop ratio estimators. However, in this article, we attempt to use some non-conventional location measures. We have incorporated tri-mean, Hodges-Lehmann, and mid-range of the auxiliary variable for this purpose. To enhance the efficiency of the proposed mean ratio estimators, population correlation coefficient, coefficient of variation and the linear combinations of auxiliary variable have also been exploited. The properties associated with the proposed estimators are evaluated through bias and mean square errors. We also provide an empirical study for illustration and verification.</p>

scholarly journals Modified Ratio Estimators of Population Mean Based on Ranked Set Sampling

2019 ◽  
pp. 445-449 ◽  
Author(s):  
Zahid Khan ◽  
Muhammad Ismail
Keyword(s):  
Mean Square Error ◽  
Coefficient Of Variation ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Variation Coefficient ◽  
Mean Square ◽  
Population Mean ◽  
The Mean ◽  
Ratio Estimators ◽  
Coefficient Of Skewness

In this paper, we propose modified ratio estimators using some known values of coefficient of variation, coefficient of skewness and coefficient of kurtosis of auxiliary variable under ranked set sampling (RSS).  The mean square error (MSE) of the proposed ratio estimators under ranked set sampling is derived and compared with some existing ratio estimators under RSS. Through this comparison, we prove theoretically that MSC of proposed estimators is less than some existing ratio estimators in RSS under some conditions. The MSE of proposed estimators along with some existing estimator are also calculated numerically. We observe from numerical results that the suggested ratio estimators are more efficient than some existing ratio estimators under RSS.

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

2021 ◽  
pp. 13-29
Author(s):  
A. Audu ◽  
M. A. Yunusa ◽  
O. O. Ishaq ◽  
M. K. Lawal ◽  
A. Rashida ◽  
...  
Keyword(s):  
Coefficient Of Variation ◽  
Finite Population ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Population Variance ◽  
Study Variable ◽  
Difference Type ◽  
Ratio Estimators ◽  
Mean Square Errors ◽  
Two Populations

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.

scholarly journals Enhanced Mean Ratio Estimators of Auxiliary Variables Based on the Linear Mixture of Variances

2019 ◽  
pp. 1-16
Author(s):  
Damisa A. Saddam ◽  
Jamila Abdullahi ◽  
Umar Nura
Keyword(s):  
Mean Square Error ◽  
Coefficient Of Variation ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Auxiliary Variables ◽  
Population Variance ◽  
Constant Bias ◽  
Ratio Estimators ◽  
Better Than

This paper incorporates the variance of auxiliary variables to propose three improved ratio estimators of population mean. To enhance the efficiency of the proposed ratio estimators, a linear combination of the population coefficient of variation, kurtosis, skewness and the population variance of the auxiliary variable is harnessed. The properties relating to the suggested estimators are assessed using constant, bias and mean square error. We also provided practical study for illustration and corroboration using a population data consisting of the fixed capital, which is the supporting variable and output of 80 factories which are the study variables. The suggested improved ratio estimators performed better than other ratio estimators in the literature when compared using bias and mean square error.

400-P: High Levels of Glycemic Coefficient of Variation Are Associated with Higher Hypoglycemia Episodes in T1D Adults in a Brazilian Tertiary Hospital

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 400-P
Author(s):  
THAIS B. BRASIL ◽  
ANDREI C. SPOSITO ◽  
BEATRIZ ADACHI ◽  
WALKYRIA M. VOLPINI ◽  
ELIZABETH J. PAVIN
Keyword(s):  
Coefficient Of Variation ◽  
Tertiary Hospital
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