52,314 research outputs found
Nonparametric Bayesian multiple testing for longitudinal performance stratification
This paper describes a framework for flexible multiple hypothesis testing of
autoregressive time series. The modeling approach is Bayesian, though a blend
of frequentist and Bayesian reasoning is used to evaluate procedures.
Nonparametric characterizations of both the null and alternative hypotheses
will be shown to be the key robustification step necessary to ensure reasonable
Type-I error performance. The methodology is applied to part of a large
database containing up to 50 years of corporate performance statistics on
24,157 publicly traded American companies, where the primary goal of the
analysis is to flag companies whose historical performance is significantly
different from that expected due to chance.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS252 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Symmetric complex-valued RBF receiver for multiple-antenna aided wireless systems
A nonlinear beamforming assisted detector is proposed for multiple-antenna-aided wireless systems employing complex-valued quadrature phase shift-keying modulation. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a novel complex-valued symmetric radial basis function (SRBF)-network-based detector is developed, which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be efficiently implemented by estimating the system’s channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variationenhanced clustering algorithm to directly identify the SRBF center vectors required for realizing the optimal Bayesian detector. A simulation example is included to demonstrate the achievable performance improvement by the proposed adaptive nonlinear beamforming solution over the theoretical linear minimum bit error rate beamforming benchmark
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
We develop a sequential low-complexity inference procedure for Dirichlet
process mixtures of Gaussians for online clustering and parameter estimation
when the number of clusters are unknown a-priori. We present an easily
computable, closed form parametric expression for the conditional likelihood,
in which hyperparameters are recursively updated as a function of the streaming
data assuming conjugate priors. Motivated by large-sample asymptotics, we
propose a novel adaptive low-complexity design for the Dirichlet process
concentration parameter and show that the number of classes grow at most at a
logarithmic rate. We further prove that in the large-sample limit, the
conditional likelihood and data predictive distribution become asymptotically
Gaussian. We demonstrate through experiments on synthetic and real data sets
that our approach is superior to other online state-of-the-art methods.Comment: 25 pages, To appear in Advances in Neural Information Processing
Systems (NIPS) 201
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