An expectation–maximization scheme for measurement error models

We treat the problem of maximum likelihood estimation in measurement error models. Direct maximization of the analytically intractable likelihood in such models is difficult. We derive an efficient expectation–maximization scheme for truncated polynomial spline models of degree one. Simulation results confirm the effectiveness of the proposed method.