Large sample theory of maximum likelihood estimates in semiparametric biased sampling models

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On empirical likelihood for semiparametric two-sample density ratio models (2008, background): “Gilbert (2000) and Gilbert et al. (1999) considered weight functions depending on an unknown finite dimensional parameter.”
Merging information for semiparametric density estimation (2004, background): “Furthermore, the asymptotic theory of Section 2 parallels the work of Gilbert (2000) who obtained large sample results for biased sampling models with weights depending on an unknown finite dimensional parameter based on a two-step estimation procedure.”
Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling (2003, unknown): “Vardi, and Wellner (1988), their semiparametric generalizations treated by Gilbert (2000) and to the \partially censored" mixture models studied by Van der Vaart and Wellner (1992) and Van der Vaart (1994).”