• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: -0001, Volume: 15, Issue: 7, Pages: 276-291

Original Article

An Improved Class of Mixed Estimators of Population Mean under Double Sampling

Received Date:29 July 2021, Accepted Date:05 January 2022, Published Date:30 November -0001


Objectives: To estimate the finite populations mean using two auxiliary variables in double sampling as well as the efficiency of the proposed class of estimators. Methods: The mixing of estimators became more popular in developing more efficient estimators for estimating finite population parameters but while mixing two or more estimators, we should consider the basic purpose and the conditions under which the individual estimators are developed and are efficient. This paper deals with a class of mixed estimators of population mean by mixing ratio estimator and dual to product estimator in two phase sampling scheme using SRSWOR scheme to select the sample units at both the cases, i.e. Case-I: When 􀀀 X is unknown but 􀀀 Z is known and Case-II: When both 􀀀 X and 􀀀 Z are unknown. The purpose of mixing these two estimators is that both these estimators are designed to be used effectively for population mean when the population correlation coefficient between the study variable and the auxiliary variable is highly positive. Results: We observe that the proposed class of estimators is more efficient than the existing estimators which are available in literature and the empirical study indicates that the proposed class of estimators t01 and t02 performs better than the other existing estimators of t ′ 1; t ′ 2; t ′ 3; t ′ 4; t ′ 5; t ′ 6; t ′ 7; t ′ 8 and t ′ 9. Novelty: The percent relative efficiency (PRE) of the proposed class of estimators in Case-II i.e., t02 is superior than the estimator proposed in Case-I t01 for all the populations except Population 1 and 10, which needs further rigorous attention to compare the performances of t01 and t02:

Keywords: Double sampling; Class of estimators; Bias; Mean square error (MSE); Percent relative efficiency (PRE)


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© 2022 Dash & Sunani. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)


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