Gareth M. James
Improved variable selection with Forward-Lasso adaptive shrinkage.
Tue, 04/19/2011 - 15:01 — SomNambulAuthors: Gareth M. James, Peter Radchenko
Subjects: Applications
AbstractRecently, considerable interest has focused on variable selection methods in
regression situations where the number of predictors, $p$, is large relative to
the number of observations, $n$. Two commonly applied variable selection
approaches are the Lasso, which computes highly shrunk regression coefficients,
and Forward Selection, which uses no shrinkage. We propose a new approach,
"Forward-Lasso Adaptive SHrinkage" (FLASH), which includes the Lasso and
Forward Selection as special cases, and can be used in both the linear
regression and the Generalized Linear Model domains.A multivariate adaptive stochastic search method for dimensionality reduction in classification.
Fri, 10/08/2010 - 15:01 — SomNambulAuthors: Gareth M. James, Tian Siva Tian, Rand R. Wilcox
Subjects: Applications
AbstractHigh-dimensional classification has become an increasingly important problem.
In this paper we propose a "Multivariate Adaptive Stochastic Search" (MASS)
approach which first reduces the dimension of the data space and then applies a
standard classification method to the reduced space. One key advantage of MASS
is that it automatically adjusts to mimic variable selection type methods, such
as the Lasso, variable combination methods, such as PCA, or methods that
combine these two approaches.Functional linear regression that's interpretable.
Fri, 08/21/2009 - 19:51 — SomNambulAuthors: Gareth M. James, Jing Wang, Ji Zhu
Subjects: gr. Statistics
AbstractRegression models to relate a scalar $Y$ to a functional predictor $X(t)$ are
becoming increasingly common. Work in this area has concentrated on estimating
a coefficient function, $\beta(t)$, with $Y$ related to $X(t)$ through
$\int\beta(t)X(t) dt$. Regions where $\beta(t)\ne0$ correspond to places where
there is a relationship between $X(t)$ and $Y$. Alternatively, points where
$\beta(t)=0$ indicate no relationship.
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