2,398 research outputs found
A method for predicting launch vehicle vibration levels in the region of the spacecraft adaptor
Prediction curves for launch vehicle vibration levels in region of spacecraft adapto
Comment on "Systematics of the Induced Magnetic Moments in 5d Layers and the Violation of the Third Hund's Rule"
Comment on F. Wilhelm et al., Phys. Rev. Lett. 87, 207202 (2001)Comment: 1 pag
The Cross-Validated Adaptive Epsilon-Net Estimator
Suppose that we observe a sample of independent and identically distributed realizations of a random variable. Assume that the parameter of interest can be defined as the minimizer, over a suitably defined parameter space, of the expectation (with respect to the distribution of the random variable) of a particular (loss) function of a candidate parameter value and the random variable. Examples of commonly used loss functions are the squared error loss function in regression and the negative log-density loss function in density estimation. Minimizing the empirical risk (i.e., the empirical mean of the loss function) over the entire parameter space typically results in ill-defined or too variable estimators of the parameter of interest (i.e., the risk minimizer for the true data generating distribution). In this article, we propose a cross-validated epsilon-net estimation methodology that covers a broad class of estimation problems, including multivariate outcome prediction and multivariate density estimation. An epsilon-net sieve of a subspace of the parameter space is defined as a collection of finite sets of points, the epsilon-nets indexed by epsilon, which approximate the subspace up till a resolution of epsilon. Given a collection of subspaces of the parameter space, one constructs an epsilon-net sieve for each of the subspaces. For each choice of subspace and each value of the resolution epsilon, one defines a candidate estimator as the minimizer of the empirical risk over the corresponding epsilon-net. The cross-validated epsilon-net estimator is then defined as the candidate estimator corresponding to the choice of subspace and epsilon-value minimizing the cross-validated empirical risk. We derive a finite sample inequality which proves that the proposed estimator achieves the adaptive optimal minimax rate of convergence, where the adaptivity is achieved by considering epsilon-net sieves for various subspaces. We also address the implementation of the cross-validated epsilon-net estimation procedure. In the context of a linear regression model, we present results of a preliminary simulation study comparing the cross-validated epsilon-net estimator to the cross-validated L^1-penalized least squares estimator (LASSO) and the least angle regression estimator (LARS). Finally, we discuss generalizations of the proposed estimation methodology to censored data structures
Local Environment of Ferromagnetically Ordered Mn in Epitaxial InMnAs
The magnetic properties of the ferromagnetic semiconductor In0.98Mn0.02As
were characterized by x-ray absorption spectroscopy and x-ray magnetic circular
dichroism. The Mn exhibits an atomic-like L2,3 absorption spectrum that
indicates that the 3d states are highly localized. In addition, a large
dichroism at the Mn L2,3 edge was observed from 5-300 K at an applied field of
2T. A calculated spectrum assuming atomic Mn2+ yields the best agreement with
the experimental InMnAs spectrum. A comparison of the dichroism spectra of MnAs
and InMnAs show clear differences suggesting that the ferromagnetism observed
in InMnAs is not due to hexagonal MnAs clusters. The temperature dependence of
the dichroism indicates the presence of two ferromagnetic species, one with a
transition temperature of 30 K and another with a transition temperature in
excess of 300 K. The dichroism spectra are consistent with the assignment of
the low temperature species to random substitutional Mn and the high
temperature species to Mn near-neighbor pairs.Comment: 10 pages, 4 figures, accepted by Applied Physics Letter
Effect of breastfeeding on gastrointestinal infection in infants: A targeted maximum likelihood approach for clustered longitudinal data
The PROmotion of Breastfeeding Intervention Trial (PROBIT) cluster-randomized
a program encouraging breastfeeding to new mothers in hospital centers. The
original studies indicated that this intervention successfully increased
duration of breastfeeding and lowered rates of gastrointestinal tract
infections in newborns. Additional scientific and popular interest lies in
determining the causal effect of longer breastfeeding on gastrointestinal
infection. In this study, we estimate the expected infection count under
various lengths of breastfeeding in order to estimate the effect of
breastfeeding duration on infection. Due to the presence of baseline and
time-dependent confounding, specialized "causal" estimation methods are
required. We demonstrate the double-robust method of Targeted Maximum
Likelihood Estimation (TMLE) in the context of this application and review some
related methods and the adjustments required to account for clustering. We
compare TMLE (implemented both parametrically and using a data-adaptive
algorithm) to other causal methods for this example. In addition, we conduct a
simulation study to determine (1) the effectiveness of controlling for
clustering indicators when cluster-specific confounders are unmeasured and (2)
the importance of using data-adaptive TMLE.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS727 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Semiparametric theory and empirical processes in causal inference
In this paper we review important aspects of semiparametric theory and
empirical processes that arise in causal inference problems. We begin with a
brief introduction to the general problem of causal inference, and go on to
discuss estimation and inference for causal effects under semiparametric
models, which allow parts of the data-generating process to be unrestricted if
they are not of particular interest (i.e., nuisance functions). These models
are very useful in causal problems because the outcome process is often complex
and difficult to model, and there may only be information available about the
treatment process (at best). Semiparametric theory gives a framework for
benchmarking efficiency and constructing estimators in such settings. In the
second part of the paper we discuss empirical process theory, which provides
powerful tools for understanding the asymptotic behavior of semiparametric
estimators that depend on flexible nonparametric estimators of nuisance
functions. These tools are crucial for incorporating machine learning and other
modern methods into causal inference analyses. We conclude by examining related
extensions and future directions for work in semiparametric causal inference
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