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