Open AccessA modified class of exponential-type estimator of population-mean in simple random sampling
Author(s) -
Ekaette Inyang Enang,
J. O. Uket,
Emmanuel John Ekpenyong
Publication year - 2017
Publication title -
international journal of advanced statistics and probability
Language(s) - English
Resource type - Journals
ISSN - 2307-9045
DOI - 10.14419/ijasp.v5i2.7345
Subject(s) - estimator , simple random sample , mean squared error , exponential type , mathematics , extremum estimator , population mean , class (philosophy) , statistics , exponential function , simple (philosophy) , ratio estimator , population , type (biology) , minimum mean square error , sampling (signal processing) , efficiency , efficient estimator , m estimator , computer science , minimum variance unbiased estimator , mathematical analysis , artificial intelligence , philosophy , ecology , demography , epistemology , filter (signal processing) , sociology , computer vision , biology
The problem of obtaining better ratio estimators of the population means are dominating in survey sampling. This paper provides a modified class of exponential type estimators using combinations of some existing estimators. Expressions for the bias and Mean Square Error (MSE) with the optimality conditions for this class of estimators have been established. Both analytical and numerical comparison with some existing estimators shows better performances from members of the proposed class.