103,525 research outputs found
Asymptotic equivalence and adaptive estimation for robust nonparametric regression
Asymptotic equivalence theory developed in the literature so far are only for
bounded loss functions. This limits the potential applications of the theory
because many commonly used loss functions in statistical inference are
unbounded. In this paper we develop asymptotic equivalence results for robust
nonparametric regression with unbounded loss functions. The results imply that
all the Gaussian nonparametric regression procedures can be robustified in a
unified way. A key step in our equivalence argument is to bin the data and then
take the median of each bin. The asymptotic equivalence results have
significant practical implications. To illustrate the general principles of the
equivalence argument we consider two important nonparametric inference
problems: robust estimation of the regression function and the estimation of a
quadratic functional. In both cases easily implementable procedures are
constructed and are shown to enjoy simultaneously a high degree of robustness
and adaptivity. Other problems such as construction of confidence sets and
nonparametric hypothesis testing can be handled in a similar fashion.Comment: Published in at http://dx.doi.org/10.1214/08-AOS681 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Minimax estimation with thresholding and its application to wavelet analysis
Many statistical practices involve choosing between a full model and reduced
models where some coefficients are reduced to zero. Data were used to select a
model with estimated coefficients. Is it possible to do so and still come up
with an estimator always better than the traditional estimator based on the
full model? The James-Stein estimator is such an estimator, having a property
called minimaxity. However, the estimator considers only one reduced model,
namely the origin. Hence it reduces no coefficient estimator to zero or every
coefficient estimator to zero. In many applications including wavelet analysis,
what should be more desirable is to reduce to zero only the estimators smaller
than a threshold, called thresholding in this paper. Is it possible to
construct this kind of estimators which are minimax? In this paper, we
construct such minimax estimators which perform thresholding. We apply our
recommended estimator to the wavelet analysis and show that it performs the
best among the well-known estimators aiming simultaneously at estimation and
model selection. Some of our estimators are also shown to be asymptotically
optimal.Comment: Published at http://dx.doi.org/10.1214/009053604000000977 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Law of Log Determinant of Sample Covariance Matrix and Optimal Estimation of Differential Entropy for High-Dimensional Gaussian Distributions
Differential entropy and log determinant of the covariance matrix of a
multivariate Gaussian distribution have many applications in coding,
communications, signal processing and statistical inference. In this paper we
consider in the high dimensional setting optimal estimation of the differential
entropy and the log-determinant of the covariance matrix. We first establish a
central limit theorem for the log determinant of the sample covariance matrix
in the high dimensional setting where the dimension can grow with the
sample size . An estimator of the differential entropy and the log
determinant is then considered. Optimal rate of convergence is obtained. It is
shown that in the case the estimator is asymptotically
sharp minimax. The ultra-high dimensional setting where is also
discussed.Comment: 19 page
Highlights of the TEXONO Research Program on Neutrino and Astroparticle Physics
This article reviews the research program and efforts for the TEXONO
Collaboration on neutrino and astro-particle physics. The ``flagship'' program
is on reactor-based neutrino physics at the Kuo-Sheng (KS) Power Plant in
Taiwan. A limit on the neutrino magnetic moment of \munuebar < 1.3 X 10^{-10}
\mub} at 90% confidence level was derived from measurements with a high purity
germanium detector. Other physics topics at KS, as well as the various R&D
program, are discussedComment: 10 pages, 9 figures, Proceedings of the International Symposium on
Neutrino and Dark Matter in Nuclear Physics (NDM03), Nara, Japan, June 9-14,
200
Quantum fluctuations in the spiral phase of the Hubbard model
We study the magnetic excitations in the spiral phase of the two--dimensional
Hubbard model using a functional integral method. Spin waves are strongly
renormalized and a line of near--zeros is observed in the spectrum around the
spiral pitch . The possibility of disordered spiral states is
examined by studying the one--loop corrections to the spiral order parameter.
We also show that the spiral phase presents an intrinsic instability towards an
inhomogeneous state (phase separation, CDW, ...) at weak doping. Though phase
separation is suppressed by weak long--range Coulomb interactions, the CDW
instability only disappears for sufficiently strong Coulomb interaction.Comment: Figures are NOW appended via uuencoded postscript fil
Ground-state configuration space heterogeneity of random finite-connectivity spin glasses and random constraint satisfaction problems
We demonstrate through two case studies, one on the p-spin interaction model
and the other on the random K-satisfiability problem, that a heterogeneity
transition occurs to the ground-state configuration space of a random
finite-connectivity spin glass system at certain critical value of the
constraint density. At the transition point, exponentially many configuration
communities emerge from the ground-state configuration space, making the
entropy density s(q) of configuration-pairs a non-concave function of
configuration-pair overlap q. Each configuration community is a collection of
relatively similar configurations and it forms a stable thermodynamic phase in
the presence of a suitable external field. We calculate s(q) by the
replica-symmetric and the first-step replica-symmetry-broken cavity methods,
and show by simulations that the configuration space heterogeneity leads to
dynamical heterogeneity of particle diffusion processes because of the entropic
trapping effect of configuration communities. This work clarifies the fine
structure of the ground-state configuration space of random spin glass models,
it also sheds light on the glassy behavior of hard-sphere colloidal systems at
relatively high particle volume fraction.Comment: 26 pages, 9 figures, submitted to Journal of Statistical Mechanic
Solution space heterogeneity of the random K-satisfiability problem: Theory and simulations
The random K-satisfiability (K-SAT) problem is an important problem for
studying typical-case complexity of NP-complete combinatorial satisfaction; it
is also a representative model of finite-connectivity spin-glasses. In this
paper we review our recent efforts on the solution space fine structures of the
random K-SAT problem. A heterogeneity transition is predicted to occur in the
solution space as the constraint density alpha reaches a critical value
alpha_cm. This transition marks the emergency of exponentially many solution
communities in the solution space. After the heterogeneity transition the
solution space is still ergodic until alpha reaches a larger threshold value
alpha_d, at which the solution communities disconnect from each other to become
different solution clusters (ergodicity-breaking). The existence of solution
communities in the solution space is confirmed by numerical simulations of
solution space random walking, and the effect of solution space heterogeneity
on a stochastic local search algorithm SEQSAT, which performs a random walk of
single-spin flips, is investigated. The relevance of this work to glassy
dynamics studies is briefly mentioned.Comment: 11 pages, 4 figures. Final version as will appear in Journal of
Physics: Conference Series (Proceedings of the International Workshop on
Statistical-Mechanical Informatics, March 7-10, 2010, Kyoto, Japan
Optimal rates of convergence for covariance matrix estimation
Covariance matrix plays a central role in multivariate statistical analysis.
Significant advances have been made recently on developing both theory and
methodology for estimating large covariance matrices. However, a minimax theory
has yet been developed. In this paper we establish the optimal rates of
convergence for estimating the covariance matrix under both the operator norm
and Frobenius norm. It is shown that optimal procedures under the two norms are
different and consequently matrix estimation under the operator norm is
fundamentally different from vector estimation. The minimax upper bound is
obtained by constructing a special class of tapering estimators and by studying
their risk properties. A key step in obtaining the optimal rate of convergence
is the derivation of the minimax lower bound. The technical analysis requires
new ideas that are quite different from those used in the more conventional
function/sequence estimation problems.Comment: Published in at http://dx.doi.org/10.1214/09-AOS752 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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