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).Comment: Published in at http://dx.doi.org/10.1214/193940307000000527 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

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

A global logrank test for adaptive treatment strategies based on observational studies

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102657/1/sim5987.pd

Higher Order Estimating Equations for High-dimensional Models

We introduce a new method of estimation of parameters in semiparametric and nonparametric models. The method is based on estimating equations that are $U$-statistics in the observations. The $U$-statistics are based on higher order influence functions that extend ordinary linear influence functions of the parameter of interest, and represent higher derivatives of this parameter. For parameters for which the representation cannot be perfect the method leads to a bias-variance trade-off, and results in estimators that converge at a slower than $\sqrt n$-rate. In a number of examples the resulting rate can be shown to be optimal. We are particularly interested in estimating parameters in models with a nuisance parameter of high dimension or low regularity, where the parameter of interest cannot be estimated at $\sqrt n$-rate, but we also consider efficient $\sqrt n$-estimation using novel nonlinear estimators. The general approach is applied in detail to the example of estimating a mean response when the response is not always observed

Semiparametric minimax rates

We consider the minimax rate of testing (or estimation) of nonlinear functionals defined on semiparametric models. Existing methods appear not capable of determining a lower bound on the minimax rate of testing (or estimation) for certain functionals of interest. In particular, if the semiparametric model is indexed by several infinite-dimensional parameters. To cover these examples we extend the approach of [1], which is based on comparing a “true distribution” to a convex mixture of perturbed distributions to a comparison of two convex mixtures. The first mixture is obtained by perturbing a first parameter of the model, and the second by perturbing in addition a second parameter. We apply the new result to two examples of semiparametric functionals:the estimation of a mean response when response data are missing at random, and the estimation of an expected conditional covariance functional

Nonparametric comparison of two survival functions with dependent censoring via nonparametric multiple imputation

When the event time of interest depends on the censoring time, conventional two-sample test methods, such as the log-rank and Wilcoxon tests, can produce an invalid test result. We extend our previous work on estimation using auxiliary variables to adjust for dependent censoring via multiple imputation, to the comparison of two survival distributions. To conduct the imputation, we use two working models to define a set of similar observations called the imputing risk set. One model is for the event times and the other for the censoring times. Based on the imputing risk set, a nonparametric multiple imputation method, Kaplan–Meier imputation, is used to impute a future event or censoring time for each censored observation. After imputation, the conventional nonparametric two-sample tests can be easily implemented on the augmented data sets. Simulation studies show that the sizes of the log-rank and Wilcoxon tests constructed on the imputed data sets are comparable to the nominal level and the powers are much higher compared with the tests based on the unimputed data in the presence of dependent censoring if either one of the two working models is correctly specified. The method is illustrated using AIDS clinical trial data comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. Copyright © 2008 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/61537/1/3480_ftp.pd

Ramsey interferometry with an atom laser

We present results on a free-space atom interferometer operating on the first order magnetically insensitive |F=1,mF=0> -> |F=2,mF=0> transition of Bose-condensed 87Rb atoms. A pulsed atom laser is output-coupled from a Bose-Einstein condensate and propagates through a sequence of two internal state beam splitters, realized via coherent Raman transitions between the two interfering states. We observe Ramsey fringes with a visibility close to 100% and determine the current and the potentially achievable interferometric phase sensitivity. This system is well suited to testing recent proposals for generating and detecting squeezed atomic states.Comment: published version, 8 pages, 3 figure

Mass Balance of the West Antarctic Ice-Sheet from ICESat Measurements

Mass balance estimates for 2003-2008 are derived from ICESat laser altimetry and compared with estimates for 1992-2002 derived from ERS radar altimetry. The net mass balance of 3 drainage systems (Pine Island, Thwaites/Smith, and the coast of Marie Bryd) for 2003-2008 is a loss of 100 Gt/yr, which increased from a loss of 70 Gt/yr for the earlier period. The DS including the Bindschadler and MacAyeal ice streams draining into the Ross Ice Shelf has a mass gain of 11 Gt/yr for 2003-2008, compared to an earlier loss of 70 Gt/yr. The DS including the Whillans and Kamb ice streams has a mass gain of 12 Gt/yr, including a significant thickening on the upper part of the Kamb DS, compared to a earlier gain of 6 Gt/yr (includes interpolation for a large portion of the DS). The other two DS discharging into the Ronne Ice Shelf and the northern Ellsworth Coast have a mass gain of 39 Gt/yr, compared to a gain of 4 Gt/yr for the earlier period. Overall, the increased losses of 30 Gt/yr in the Pine Island, Thwaites/Smith, and the coast of Marie Bryd DSs are exceeded by increased gains of 59 Gt/yr in the other 4 DS. Overall, the mass loss from the West Antarctic ice sheet has decreased to 38 Gt/yr from the earlier loss of 67 Gt/yr, reducing the contribution to sea level rise to 0.11 mm/yr from 0.19 mm/y