A (nonlinear) measurement error model (MEM) consists of three parts: (1) a regression model relating an observable regressor variable z and unobservable regressor variable ξ (the variables are independent and generally vector valued) to a response variable y, which is considered here to be observable without measurement errors; (2) a measurement model relating the unobservable ξ to an observable surrogate variable x; and (3) a distributional model for ξ.