  This paper deals with the problem of estimating a slope parameter in a simple
linear regression model, where independent variables have functional
measurement errors. Measurement errors in independent variables, as is well
known, cause biasedness of the ordinary least squares estimator. A general
procedure for the bias reduction is presented in a finite sample situation, and
some exact bias-reduced estimators are proposed. Also, it is shown that certain
truncation procedures improve the mean square errors of the ordinary least
squares and the bias-reduced estimators.
