Relative Efficiency of Maximum Likelihood and Other Estimators in a Nonlinear Regression Model with Small Measurement Errors

We compare the asymptotic covariance matrix of the ML estimator in
a nonlinear measurement error model to the asymptotic covariance
matrices of the CS and SQS estimators studied in Kukush et al
(2002). For small measurement error variances they are equal up to
the order of the measurement error variance and thus nearly equally
efficient.