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2019•Zenodo (CERN European Organization for Nuclear Research)Open access

LOG-TYPE ESTIMATORS FOR ESTIMATING POPULATION MEAN IN SYSTEMATIC SAMPLING IN THE PRESENCE OF NON-RESPONSE

Rajesh Singh, Sakshi Rai

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Abstract

This paper utilizes the information of auxiliary variable for estimating population mean in systematic sampling under the effect of non-response in study variable. Along with the proposed estimators, the paper also discusses some existing estimators. The expressions of mean squared errors of proposed estimators are derived up to the first order of approximation and it is observed that the efficiencies of proposed estimators are better than the existing estimators. To ratify the results, an empirical study has been performed taking two data sets.

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What this paper is about

This paper utilizes the information of auxiliary variable for estimating population mean in systematic sampling under the effect of non-response in study variable. Along with the proposed estimators, the paper also discusses some existing estimators. The expressions of mean squared errors of proposed estimators are derived up to the first order of approximation and it is observed that the efficiencies of proposed estimators are better than the existing estimators. To ratify the results, an empirical study has been performed taking two data sets.

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Available abstract

This paper utilizes the information of auxiliary variable for estimating population mean in systematic sampling under the effect of non-response in study variable. Along with the proposed estimators, the paper also discusses some existing estimators. The expressions of mean squared errors of proposed estimators are derived up to the first order of approximation and it is observed that the efficiencies of proposed estimators are better than the existing estimators. To ratify the results, an empirical study has been performed taking two data sets.

Key concepts: Estimator, Population mean, Mathematics, Statistics, Extremum estimator, Mean squared error, M-estimator, Variable (mathematics)

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