Semiparametric Learning of Integral Functionals on Submanifolds

This paper studies the semiparametric estimation and inference of integral functionals on submanifolds, which arise naturally in a variety of econometric settings. For linear integral functionals on a regular submanifold, we show that the semiparametric plugin estimator attains the minimax-optimal convergence rate $n—\frac{s}{2s\mathrm{+}d\mathrm{-}m}$, where s is the Hölder smoothness order of the underlying nonparametric function, d is the dimension of the first-stage nonparametric estimation, m is the dimension of the submanifold over which the integral is taken. This rate coincides with the standard minimax-optimal rate for a (d − m)-dimensional nonparametric estimation problem, illustrating that integration over the m-dimensional manifold effectively reduces the problem’s dimensionality. We then provide a general asymptotic normality theorem for linear/nonlinear submanifold integrals, along with a consistent variance estimator. We provide simulation evidence in support of our theoretical results.