1,459 research outputs found
Lasso adjustments of treatment effect estimates in randomized experiments
We provide a principled way for investigators to analyze randomized
experiments when the number of covariates is large. Investigators often use
linear multivariate regression to analyze randomized experiments instead of
simply reporting the difference of means between treatment and control groups.
Their aim is to reduce the variance of the estimated treatment effect by
adjusting for covariates. If there are a large number of covariates relative to
the number of observations, regression may perform poorly because of
overfitting. In such cases, the Lasso may be helpful. We study the resulting
Lasso-based treatment effect estimator under the Neyman-Rubin model of
randomized experiments. We present theoretical conditions that guarantee that
the estimator is more efficient than the simple difference-of-means estimator,
and we provide a conservative estimator of the asymptotic variance, which can
yield tighter confidence intervals than the difference-of-means estimator.
Simulation and data examples show that Lasso-based adjustment can be
advantageous even when the number of covariates is less than the number of
observations. Specifically, a variant using Lasso for selection and OLS for
estimation performs particularly well, and it chooses a smoothing parameter
based on combined performance of Lasso and OLS
Determination of the star valency of a graph
AbstractThe star valency of a graph G is the minimum, over all star decompositions π, of the maximum number of elements in π incident with a vertex. The maximum average degree of G, denoted by dmax-ave(G), is the maximum average degree of all subgraphs of G. In this paper, we prove that the star valency of G is either ⌈dmax-ave(G)/2⌉ or ⌈dmax-ave(G)/2⌉+1, and provide a polynomial time algorithm for determining the star valency of a graph
Integration of surface science, nanoscience, and catalysis
This is the published version. Copyright 2010 International Union of Pure and Applied ChemistryThis article briefly reviews the development of surface science and its close relevance to nanoscience and heterogeneous catalysis. The focus of this article is to highlight the importance of nanoscale surface science for understanding heterogeneous catalysis performing at solid–gas and solid–liquid interfaces. Surface science has built a foundation for the understanding of catalysis based on the studies of well-defined single-crystal catalysts in the past several decades. Studies of catalysis on well-defined nanoparticles (NPs) significantly promoted the understanding of catalytic mechanisms to an unprecedented level in the last decade. To understand reactions performed on catalytic active sites at nano or atomic scales and thus reach the goal of catalysis by design, studies of the surface of nanocatalysts are crucial. The challenges in such studies are discussed
An electron acceptor molecule in a nanomesh: F4TCNQ on h-BN/Rh(111)
The adsorption of molecules on surfaces affects the surface dipole and thus
changes in the work function may be expected. The effect in change of work
function is particularly strong if charge between substrate and adsorbate is
involved. Here we report the deposition of a strong electron acceptor molecule,
tetrafluorotetracyanoquinodimethane CFN (FTCNQ) on a
monolayer of hexagonal boron nitride nanomesh (-BN on Rh(111)). The work
function of the FTCNQ/-BN/Rh system increases upon increasing
molecular coverage. The magnitude of the effect indicates electron transfer
from the substrate to the FTCNQ molecules. Density functional theory
calculations confirm the work function shift and predict doubly charged
FTCNQ in the nanomesh pores, where the -BN is closest to the Rh
substrate, and to have the largest binding energy there. The preferred
adsorption in the pores is conjectured from a series of ultraviolet
photoelectron spectroscopy data, where the bands in the pores are
first attenuated. Scanning tunneling microscopy measurements indicate that
FTCNQ molecules on the nanomesh are mobile at room temperature, as
"hopping" between neighboring pores is observed
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