Estimation methods for simple linear regression with measurement error: a real data application

Abstract

The classical measurement error model is discussed in the context of parameter estimation of the simple linear regression. The attenuationeğect of measurement error on the parameter estimation is eliminated usingthe regression calibration and simulation extrapolation methods. The massdensity of pebbles population is investigated as a real data application. Themass and volume of a pebble are regarded an error-free and error-prone variables, respectively. The population mass density is considered to be the slopeparameter of the simple linear regression without intercept