Published Online: 16 December 2008
Page Count: 6
Assistant Professor of Marketing, Kent State University, Kent, OH
Professor of Physics, Xihua University, Chengdu,
(Received 8 October 2008; accepted 18 November 2008)
This paper proposes a new estimator of regression coefficient in a linear model when both variables are observed with errors. Unlike previous estimators, the new estimator fully utilizes instrumental errors ratio (k) available to the experimental researcher. When compared to other estimators, our estimator is easy to use and provides an estimate with less bias. It is also a generalization of existing methods when k takes different values.
Paper ID: JTE102140