Robust inference using higher order influence functions

We present a theory of point and interval estimation for nonlinear functionals in parametric, semi-, and non-parametric models based on higher order influence functions (Robins 2004, Sec. 9, Li et al., 2006, Tchetgen et al., 2006, Robins et al., 2007). Higher order influence functions are higher order U-statistics. Our theory extends the first order semiparametric theory of Bickel et al. (1993) and van der Vaart (1991) by incorporating the theory of higher order scores considered by Pfanzagl (1990), Small and McLeish (1994), and Lindsay and Waterman (1996). The theory reproduces many previous results, produces new non-[Special characters omitted.] results, and opens up the ability to perform optimal non-[Special characters omitted.] inference in complex high dimensional models. We present novel rate-optimal point and interval estimators for various functionals of central importance to biostatistics in settings in which estimation at the expected [Special characters omitted.] rate is not possible, owing to the curse of dimensionality. We also show that our higher order influence functions have a multi-robustness property that extends the double robustness property of first order influence functions described by Robins and Rotnitzky (2001) and van der Laan and Robins (2003).