scholarly journals On The Efficiency of Almost Unbiased Mean Imputation When Population Mean of Auxiliary Variable is UnknownOn The Efficiency of Almost Unbiased Mean Imputation When Population Mean of Auxiliary Variable is Unknown

2021 ◽  
pp. 235-250
Author(s):  
A. Audu ◽  
A. Danbaba ◽  
S. K. Ahmad ◽  
N. Musa ◽  
A. Shehu ◽  
...  
Keyword(s):  
Order Approximation ◽  
Taylor Series Expansion ◽  
Auxiliary Variable ◽  
Missing Observations ◽  
Population Mean ◽  
Mean Imputation ◽  
Expansion Technique ◽  
Class Of Estimators ◽  
First Order Approximation ◽  
Almost Unbiased

Human-assisted surveys, such as medical and social science surveys, are frequently plagued by non-response or missing observations. Several authors have devised different imputation algorithms to account for missing observations during analyses. Nonetheless, several of these imputation schemes' estimators are based on known population meanof auxiliary variable. In this paper, a new class of almost unbiased imputation method that uses  as an estimate of is suggested. Using the Taylor series expansion technique, the MSE of the class of estimators presented was derived up to first order approximation. Conditions were also specified for which the new estimators were more efficient than the other estimators studied in the study. The results of numerical examples through simulations revealed that the suggested class of estimators is more efficient.

scholarly journals Exponential-Ratio-Type Imputation Class of Estimators using Nonconventional Robust Measures of Dispersions

2021 ◽  
pp. 59-74
Author(s):  
Ahmed Audu ◽  
Mojeed Abiodun Yunusa ◽  
Aminu Bello Zoramawa ◽  
Samaila Buda ◽  
Ran Vijay Kumar Singh
Keyword(s):  
Order Approximation ◽  
Taylor Series Expansion ◽  
Auxiliary Variable ◽  
Missing Observations ◽  
Variable Parameters ◽  
First Order ◽  
Expansion Technique ◽  
Class Of Estimators ◽  
First Order Approximation ◽  
Robust Measures

Human-assisted surveys, such as medical and social science surveys, are frequently plagued by non-response or missing observations. Several authors have devised different imputation algorithms to account for missing observations during analyses. Nonetheless, several of these imputation schemes' estimators are based on known auxiliary variable parameters that can be influenced by outliers. In this paper, we suggested new classes of exponential-ratio-type imputation method that uses parameters that are robust against outliers. Using the Taylor series expansion technique, the MSE of the class of estimators presented was derived up to first order approximation. Conditions were also specified for which the new estimators were more efficient than the other estimators studied in the study. The results of numerical examples through simulations revealed that the suggested class of estimators is more efficient.

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

2021 ◽  
Vol 17 (2) ◽  
pp. 75-90
Author(s):  
B. Prashanth ◽  
K. Nagendra Naik ◽  
R. Salestina M
Keyword(s):  
Characteristic Polynomial ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Sampling Scheme ◽  
Signed Graph ◽  
Population Mean ◽  
Class Of Estimators

Abstract With this article in mind, we have found some results using eigenvalues of graph with sign. It is intriguing to note that these results help us to find the determinant of Normalized Laplacian matrix of signed graph and their coe cients of characteristic polynomial using the number of vertices. Also we found bounds for the lowest value of eigenvalue.

scholarly journals Efficient Generalized Ratio-Product Type Estimators for Finite Population Mean with Ranked Set Sampling

2016 ◽  
Vol 42 (3) ◽  
pp. 137-148 ◽  
Author(s):  
V.L. Mandowara ◽  
Nitu Mehta
Keyword(s):  
Mean Squared Error ◽  
Order Approximation ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Power Transformation ◽  
Numerical Illustration ◽  
Population Mean ◽  
Squared Error ◽  
The Mean ◽  
First Order Approximation

In this paper we suggest two modified estimators of the population mean using the power transformation based on ranked set sampling (RSS). The first order approximation of the bias and of the mean squared error of the proposed estimators are obtained. A generalized version of the suggested estimators by applying the power transformation is also presented. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in simple random sampling (SRS). A numerical illustration is also carried out to demonstrate the merits of the proposed estimators using RSS over the usual estimators in SRS.

scholarly journals Modified Class of Estimator for Finite Population Mean Under Two-Phase Sampling Using Regression Estimation Approach

2021 ◽  
pp. 52-64
Author(s):  
A. Y. Erinola ◽  
R. V. K. Singh ◽  
A. Audu ◽  
T. James
Keyword(s):  
Order Approximation ◽  
Simple Random Sampling ◽  
Study Variable ◽  
Two Phase ◽  
Population Mean ◽  
The Mean ◽  
First Order Approximation ◽  
Mean Square Errors ◽  
Percentage Relative Efficiency ◽  
Taylor’S Series

This study proposed modified a class of estimator in simple random sampling for the estimation of population mean of the study variable using as axillary information. The biases and MSE of suggested estimators were derived up to the first order approximation using Taylor’s series expansion approach. Theoretically, the suggested estimators were compared with the existing estimators in the literature. The mean square errors (MSE) and percentage relative efficiency (PRE) of proposed estimators and that of some existing estimators were computed numerically and the results revealed that the members of the proposed class of estimator were more efficient compared to their counterparts and can produce better estimates than other estimators considered in the study.

scholarly journals Regression-type Imputation Class of Estimators using Auxiliary Attributes

2021 ◽  
pp. 1-13
Author(s):  
A. Audu ◽  
O. O. Ishaq ◽  
A. Abubakar ◽  
K. A. Akintola ◽  
U. Isah ◽  
...  
Keyword(s):  
Order Approximation ◽  
Quantitative Information ◽  
Sample Survey ◽  
Qualitative Information ◽  
Mean Squared Errors ◽  
First Order ◽  
Class Of Estimators ◽  
The Mean ◽  
First Order Approximation ◽  
Two Populations

Several imputation schemes and estimators have been proposed by different authors in sample survey. However, these estimators utilized quantitative information of auxiliary characters. In this study, some imputation methods were studied using qualitative information of auxiliary characters and two new imputation schemes using auxiliary attribute have been suggested. The mean squared errors of the proposed estimators were derived up to first order approximation using Taylor series approach. Numerical illustrations with two populations were conducted and the results revealed that the proposed estimator is more efficient.

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 An Alternative Estimator for Estimating the Finite Population Mean Using Auxiliary Information in Sample Surveys

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ramkrishna S. Solanki ◽  
Housila P. Singh ◽  
Anjana Rathour
Keyword(s):  
Finite Population ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Population Mean ◽  
Asymptotic Expressions ◽  
Variance Formula ◽  
Optimum Estimator ◽  
Class Of Estimators ◽  
Alternative Estimator ◽  
Usual Product

This paper suggests a class of estimators for estimating the finite population mean of the study variable using known population mean of the auxiliary variable . Asymptotic expressions of bias and variance of the suggested class of estimators have been obtained. Asymptotic optimum estimator (AOE) in the class is identified along with its variance formula. It has been shown that the proposed class of estimators is more efficient than usual unbiased, usual ratio, usual product, Bahl and Tuteja (1991), and Kadilar and Cingi (2003) estimators under some realistic conditions. An empirical study is carried out to judge the merits of suggested estimator over other competitors practically.

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