Profile Likelihood Estimation in Semi-Parametric Models

Yuichi Hirose

This talk presents an alternative profile likelihood estimation theory. By introducing a new parametrization, we improve on the seminal work of Murphy and van der Vaart (2000) in 2 ways: we prove the no bias condition in a general semi-parametric model context, and deal with the direct quadratic expansion of the profile likelihood rather than an approximate one. In addition, we discuss a difficulty which we encounter in the profile likelihood estimation.

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