Maximum likelihood estimators for incorrect models, with an application to ascertainment bias for continuous characters - Sawyer, S. (Elsevier)
Uncertainty about the ascertainment of human family data leads to a need for robust methods for estimating genetic and environmental effects. This in turn leads to a need for efficient techniques for estimating model parameters for data generated under one parametric model but analyzed under a second model. If the two models correspond to different ascertainment schemes for the same exponential family, simple formulas for the asymptotic means and standard errors of both conditional and unconditional MLEs can be derived. In an example for continuous sibship data, these formulas show that estimates derived from conditioning on proband value have greater asymptotic bias than two other estimators. Similarly, either conditioning on proband value or conditioning on the number of affected family members resulted in biases of up to 30% when ascertainment depended on the values of more than one affected family member.
http://dx.doi.org/10.1016/0040-5809(90)90019-R
Keywords: W