8,015 research outputs found
Systems of interacting diffusions with partial annihilation through membranes
We introduce an interacting particle system in which two families of
reflected diffusions interact in a singular manner near a deterministic
interface . This system can be used to model the transport of positive and
negative charges in a solar cell or the population dynamics of two segregated
species under competition. A related interacting random walk model with
discrete state spaces has recently been introduced and studied in Chen and Fan
(2014). In this paper, we establish the functional law of large numbers for
this new system, thereby extending the hydrodynamic limit in Chen and Fan
(2014) to reflected diffusions in domains with mixed-type boundary conditions,
which include absorption (harvest of electric charges). We employ a new and
direct approach that avoids going through the delicate BBGKY hierarchy.Comment: Published at http://dx.doi.org/10.1214/15-AOP1047 in the Annals of
Probability (http://www.imstat.org/aop/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Necessary and sufficient conditions for consistent root reconstruction in Markov models on trees
We establish necessary and sufficient conditions for consistent root
reconstruction in continuous-time Markov models with countable state space on
bounded-height trees. Here a root state estimator is said to be consistent if
the probability that it returns to the true root state converges to 1 as the
number of leaves tends to infinity. We also derive quantitative bounds on the
error of reconstruction. Our results answer a question of Gascuel and Steel and
have implications for ancestral sequence reconstruction in a classical
evolutionary model of nucleotide insertion and deletion.Comment: 30 pages, 3 figures, title of reference [FR] is update
Genealogies in Expanding Populations
The goal of this paper is to prove rigorous results for the behavior of
genealogies in a one-dimensional long range biased voter model introduced by
Hallatschek and Nelson [25]. The first step, which is easily accomplished using
results of Mueller and Tribe [38], is to show that when space and time are
rescaled correctly, our biased voter model converges to a Wright-Fisher SPDE. A
simple extension of a result of Durrett and Restrepo [18] then shows that the
dual branching coalescing random walk converges to a branching Brownian motion
in which particles coalesce after an exponentially distributed amount of
intersection local time. Brunet et al. [8] have conjectured that genealogies in
models of this type are described by the Bolthausen-Sznitman coalescent, see
[39]. However, in the model we study there are no simultaneous coalescences.
Our third and most significant result concerns "tracer dynamics" in which some
of the initial particles in the biased voter model are labeled. We show that
the joint distribution of the labeled and unlabeled particles converges to the
solution of a system of stochastic partial differential equations. A new
duality equation that generalizes the one Shiga [44] developed for the
Wright-Fisher SPDE is the key to the proof of that result.Comment: 40 pages, 1 figur
Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification
We propose a high dimensional classification method that involves
nonparametric feature augmentation. Knowing that marginal density ratios are
the most powerful univariate classifiers, we use the ratio estimates to
transform the original feature measurements. Subsequently, penalized logistic
regression is invoked, taking as input the newly transformed or augmented
features. This procedure trains models equipped with local complexity and
global simplicity, thereby avoiding the curse of dimensionality while creating
a flexible nonlinear decision boundary. The resulting method is called Feature
Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by
generalizing the Naive Bayes model, writing the log ratio of joint densities as
a linear combination of those of marginal densities. It is related to
generalized additive models, but has better interpretability and computability.
Risk bounds are developed for FANS. In numerical analysis, FANS is compared
with competing methods, so as to provide a guideline on its best application
domain. Real data analysis demonstrates that FANS performs very competitively
on benchmark email spam and gene expression data sets. Moreover, FANS is
implemented by an extremely fast algorithm through parallel computing.Comment: 30 pages, 2 figure
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