Bias and mean square error reduction by changing the shape of the distribution of an auxiliary variable: application to air pollution data in Nan, Thailand
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引用次数: 2
Abstract
ABSTRACT The proposed estimator of the population mean is based on a modification of the shape of the distribution of an auxiliary variable. If the theoretical correlation between the study and the auxiliary variables is less than a term that is proportional to the coefficient of variation of the auxiliary variable divided by the coefficient of variation of the study variable, then the modification of the distribution of the auxiliary variable reduces the bias and the mean square error of the estimator. A simulation confirms the analytical results. Application to air pollution data in Nan, Thailand, shows that on average, the biases of the estimators based on the modified auxiliary variable are reduced by 70% to 98% and the mean square errors by 91% to 100% compared to the estimators based on the unmodified auxiliary variable.
期刊介绍：
Mathematical Population Studies publishes carefully selected research papers in the mathematical and statistical study of populations. The journal is strongly interdisciplinary and invites contributions by mathematicians, demographers, (bio)statisticians, sociologists, economists, biologists, epidemiologists, actuaries, geographers, and others who are interested in the mathematical formulation of population-related questions.
The scope covers both theoretical and empirical work. Manuscripts should be sent to Manuscript central for review. The editor-in-chief has final say on the suitability for publication.
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