16,873 research outputs found
Confidence regions for high quantiles of a heavy tailed distribution
Estimating high quantiles plays an important role in the context of risk
management. This involves extrapolation of an unknown distribution function. In
this paper we propose three methods, namely, the normal approximation method,
the likelihood ratio method and the data tilting method, to construct
confidence regions for high quantiles of a heavy tailed distribution. A
simulation study prefers the data tilting method.Comment: Published at http://dx.doi.org/10.1214/009053606000000416 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
EigenGP: Gaussian Process Models with Adaptive Eigenfunctions
Gaussian processes (GPs) provide a nonparametric representation of functions.
However, classical GP inference suffers from high computational cost for big
data. In this paper, we propose a new Bayesian approach, EigenGP, that learns
both basis dictionary elements--eigenfunctions of a GP prior--and prior
precisions in a sparse finite model. It is well known that, among all
orthogonal basis functions, eigenfunctions can provide the most compact
representation. Unlike other sparse Bayesian finite models where the basis
function has a fixed form, our eigenfunctions live in a reproducing kernel
Hilbert space as a finite linear combination of kernel functions. We learn the
dictionary elements--eigenfunctions--and the prior precisions over these
elements as well as all the other hyperparameters from data by maximizing the
model marginal likelihood. We explore computational linear algebra to simplify
the gradient computation significantly. Our experimental results demonstrate
improved predictive performance of EigenGP over alternative sparse GP methods
as well as relevance vector machine.Comment: Accepted by IJCAI 201
Arc-swift: A Novel Transition System for Dependency Parsing
Transition-based dependency parsers often need sequences of local shift and
reduce operations to produce certain attachments. Correct individual decisions
hence require global information about the sentence context and mistakes cause
error propagation. This paper proposes a novel transition system, arc-swift,
that enables direct attachments between tokens farther apart with a single
transition. This allows the parser to leverage lexical information more
directly in transition decisions. Hence, arc-swift can achieve significantly
better performance with a very small beam size. Our parsers reduce error by
3.7--7.6% relative to those using existing transition systems on the Penn
Treebank dependency parsing task and English Universal Dependencies.Comment: Accepted at ACL 201
Preparation and field-induced electrical properties of perovskite relaxor ferroelectrics
(111)-oriented and random oriented Pb0.8Ba0.2ZrO3 (PBZ) perovskite relaxor ferroelectric thin films were fabricated on Pt(111)/TiOx/SiO2/Si substrate by sol-gel method. Nano-scaled antiferroelectric and ferroelectric two-phase coexisted in both (111)-oriented and random oriented PBZ thin film. High dielectric tunability (i = 75%, E = 560 kV/ cm ) and figure-of-merit (FOM ~ 236) at room temperature was obtained in (111)-oriented thin film. Meanwhile, giant electrocaloric effect (ECE) (AT = 45.3 K and AS = 46.9 JK-1kg-1 at 598 kVcm-1) at room temperature (290 K), rather than at its Curie temperature (408 K), was observed in random oriented Pb0.8Ba0.2ZrO3 (PBZ) thin film, which makes it a promising material for the application to cooling systems near room temperature. The giant ECE as well as high dielectric tunability are attributed to the coexistence of AFE and FE phases and field-induced nano-scaled AFE to FE phase transition
- …