Regression-type Imputation Class of Estimators using Auxiliary Attributes
A. Audu
Department of Mathematics, Usmanu Danfidiyo University, Sokoto, Nigeria.
O. O. Ishaq *
Department of Statistics, Kano University of Science and Technology, Wudil, Nigeria.
A. Abubakar
Department of Mathematics, Usmanu Danfidiyo University, Sokoto, Nigeria.
K. A. Akintola
Department of Statistics, Oyo State College of Agriculture and Technology, Igboora, Nigeria.
U. Isah
Department of Mathematics, Usmanu Danfidiyo University, Sokoto, Nigeria.
A. Rashida
State College of Basic and Remedial Studies, Sokoto, Nigeria.
S. Muhammed
Department of Mathematics, Usmanu Danfidiyo University, Sokoto, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
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.
Keywords: Imputation, non-response, estimator, population mean, attribute