A Combined Family of Ratio Estimators for Population Mean using an Auxiliary Variable in Simple Random Sampling
Uraiwan Jaroengeratikun, Nuanpan Lawson
Abstract
Open-access reader
Uraiwan Jaroengeratikun, Nuanpan Lawson
Abstract
Open-access reader
This paper proposes two new classes of ratio estimators for population mean when information on a known auxiliary variable is available in simple random sampling. A combined family of ratio estimators for estimating population mean by combining the two new estimators together in order to minimize the mean square error (MSE) is then suggested. The expressions for the bias and mean square error of all proposed estimators up to the first order of approximation were obtained. The performance of the proposed estimators was compared with that of existing estimators using both a theoretical and a simulation study. The proposed family of estimators was found to be more efficient than the existing estimators.
OpenAlex reports 12 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This paper proposes two new classes of ratio estimators for population mean when information on a known auxiliary variable is available in simple random sampling. A combined family of ratio estimators for estimating population mean by combining the two new estimators together in order to minimize the mean square error (MSE) is then suggested. The expressions for the bias and mean square error of all proposed estimators up to the first order of approximation were obtained. The performance of the proposed estimators was compared with that of existing estimators using both a theoretical and a simulation study. The proposed family of estimators was found to be more efficient than the existing estimators.
Key concepts: Estimator, Population mean, Simple random sample, Mean squared error, Mathematics, Ratio estimator, Statistics, Extremum estimator