24,707 research outputs found
Multiple Change-point Detection: a Selective Overview
Very long and noisy sequence data arise from biological sciences to social
science including high throughput data in genomics and stock prices in
econometrics. Often such data are collected in order to identify and understand
shifts in trend, e.g., from a bull market to a bear market in finance or from a
normal number of chromosome copies to an excessive number of chromosome copies
in genetics. Thus, identifying multiple change points in a long, possibly very
long, sequence is an important problem. In this article, we review both
classical and new multiple change-point detection strategies. Considering the
long history and the extensive literature on the change-point detection, we
provide an in-depth discussion on a normal mean change-point model from aspects
of regression analysis, hypothesis testing, consistency and inference. In
particular, we present a strategy to gather and aggregate local information for
change-point detection that has become the cornerstone of several emerging
methods because of its attractiveness in both computational and theoretical
properties.Comment: 26 pages, 2 figure
Nonparametric estimation of genewise variance for microarray data
Estimation of genewise variance arises from two important applications in
microarray data analysis: selecting significantly differentially expressed
genes and validation tests for normalization of microarray data. We approach
the problem by introducing a two-way nonparametric model, which is an extension
of the famous Neyman--Scott model and is applicable beyond microarray data. The
problem itself poses interesting challenges because the number of nuisance
parameters is proportional to the sample size and it is not obvious how the
variance function can be estimated when measurements are correlated. In such a
high-dimensional nonparametric problem, we proposed two novel nonparametric
estimators for genewise variance function and semiparametric estimators for
measurement correlation, via solving a system of nonlinear equations. Their
asymptotic normality is established. The finite sample property is demonstrated
by simulation studies. The estimators also improve the power of the tests for
detecting statistically differentially expressed genes. The methodology is
illustrated by the data from microarray quality control (MAQC) project.Comment: Published in at http://dx.doi.org/10.1214/10-AOS802 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Complementarity of LHC and EDMs for Exploring Higgs CP Violation
We analyze the constraints on a CP-violating, flavor conserving, two Higgs
doublet model from the measurements of Higgs properties and from the search for
heavy Higgs bosons at LHC, and show that the stronger limits typically come
from the heavy Higgs search channels. The limits on CP violation arising from
the Higgs sector measurements are complementary to those from EDM measurements.
Combining all current constraints from low energy to colliders, we set generic
upper bounds on the CP violating angle which parametrizes the CP odd component
in the 126 GeV Higgs boson.Comment: 34 pages, 17 figures, 2 tables; references added, constraints from
Higgs decay into tau updated, version accepted for publication in JHE
Trade Facilitation and Expanding the Benefits of Trade: Evidence from Firm Leval Data
Existing empirical studies on trade costs and trade facilitation largely focus on aggregate impacts of reform due to data availability. We take a step toward filling in this gap in literature. Using the World Bank Enterprises Surveys, the study extends the scope of empirical literature to firm dimension with a focus on SMEs.Trade Facilitation, Expanding the Benefits of Trade
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