arXiv subjects

Lingling Li

Publications and source records attributed to Lingling Li.

58 records · Page 4Linked to original sources

arXiv2016

Technical Report: Higher Order Influence Functions and Minimax Estimation of Nonlinear Functionals

Robins et al, 2008, published a theory of higher order influence functions for inference in semi- and non-parametric models. This paper is a comprehensive manuscript from which Robins et al, was drawn. The current paper includes many results and proofs that were not included in Robins et al due to space limitation. Particular results contained in the present paper that were not reported in Robins et al include the following. Given a set of functionals and their corresponding higher order influence functions, we show how to derive the higher order influence function of their product. We apply this result to obtain higher order influence functions and associated estimators for the mean of a response Y subject to monotone missingness under missing at random. These results also apply to estimating the causal effect of a time dependent treatment on an outcome Y in the presence of time-varying confounding. Finally, we include an appendix that contains proofs for all theorems that were stated without proof in Robins et al, 2008. The initial part of the paper is closely related to Robins et al, the latter parts differ.

stat.ME↗

arXiv2015

Asymptotic Normality of Quadratic Estimators

We prove conditional asymptotic normality of a class of quadratic U-statistics that are dominated by their degenerate second order part and have kernels that change with the number of observations. These statistics arise in the construction of estimators in high-dimensional semi- and non-parametric models, and in the construction of nonparametric confidence sets. This is illustrated by estimation of the integral of a square of a density or regression function, and estimation of the mean response with missing data. We show that estimators are asymptotically normal even in the case that the rate is slower than the square root of the observations.

stat.ME↗

arXiv2013

First-principles generalized gradient approximation (GGA) +Ud+Up studies of electronic structures and optical properties in cubic HfO2

The electronic structures and optical properties of cubic HfO2 are calculated by means of generalized gradient approximation (GGA) +U approach. Without on-site Coulomb interactions, the band gap of cubic HfO2 is 2.92eV, much lower than the experimental value (5.7eV). Introducing the Coulomb interactions of 5d orbitals on Hf atom (Ud) and of 2p orbitals on O atom (Up), we can reproduce the experimental value of the band gap. The calculated dielectric function of cubic HfO2 by the GGA+ Ud + Up approach predicts the presence of a shoulder structure below the main peak of the absorption spectrum. These indicate that the GGA +Ud +UP approach is a convenient and powerful method to calculate and predict the electronic structures and the optical properties of wide-gap optical materials.

cond-mat.mtrl-sci↗

arXiv2008

Higher order influence functions and minimax estimation of nonlinear functionals

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), Section 9; Li et al. (2004), 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-$\sqrt{n}$ results, and opens up the ability to perform optimal non-$\sqrt{n}$ 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 $\sqrt{n}$ 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).

math.ST↗