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Out-of-sample Extension for Latent Position Graphs
out-of-sample extension inhomogeneous random graphs latent position model convergence of eigenvectors
2013/6/14
We consider the problem of vertex classification for graphs constructed from the latent position model. It was shown previously that the approach of embedding the graphs into some Euclidean space foll...
Simple Le Cam optimal inference for the tail weight of multivariate Student $t$ distributions: testing procedures and estimation
local asymptotic normality locally asymptotically maximin tests one-step estimation Student t distribution tail weight
2013/6/14
The multivariate Student $t$ distribution is at the core of classical statistical inference and is also a well-known model for empirical financial data. In the present paper, we propose optimal (in th...
Outlier Detection via Parsimonious Mixtures of Contaminated Gaussian Distributions
Mixture models Model-based classification EM algorithm Contaminated Gaussian distribution Outlier detection Robust estimates Trimmed clustering
2013/6/14
For multivariate continuous data, the contaminated Gaussian distribution - having two parameters indicating the proportion of outliers and the degree of contamination - represents a convenient and nat...
Quantum Annealing for Dirichlet Process Mixture Models with Applications to Network Clustering
Quantum annealing Dirichlet process Stochastic optimization Maximum a posteriori estimation Bayesian nonparametrics
2013/6/17
We developed a new quantum annealing (QA) algorithm for Dirichlet process mixture (DPM) models based on the Chinese restaurant process (CRP). QA is a parallelized extension of simulated annealing (SA)...
Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families
SNML Exchangeability Exponential Family Online Learning Logarithmic Loss Bayesian Strategy Jeffreys Prior Fisher Information
2013/6/17
We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and sequential normalized m...
On Asymptotically Distribution Free Tests with Parametric Hypothesis for Ergodic Diffusion Processes
Cramer-von Mises tests ergodic diffusion process goodness of fit test asymptotically distribution free
2013/6/14
We consider the problem of the construction of the asymptotically distribution free test by the observations of ergodic diffusion process. It is supposedd that under the basic hypothesis the trend coe...
On Asymptotically Distribution Free Tests with Parametric Hypothesis for Ergodic Diffusion Processes
Cramer-von Mises tests ergodic diffusion process goodness of fit test asymptotically distribution free
2013/6/14
We consider the problem of the construction of the asymptotically distribution free test by the observations of ergodic diffusion process. It is supposedd that under the basic hypothesis the trend coe...
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices
Covariance matrix group sparsity low-rank matrix minimax rate of convergence sparse principal component analysis principal subspace,rank detection
2013/6/14
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minima...
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices
Covariance matrix group sparsity low-rank matrix minimax rate of convergence sparse principal component analysis principal subspace,rank detection
2013/6/14
This paper considers sparse spiked covariance matrix models in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minima...
Mean field variational Bayesian inference for support vector machine classification
Approximate Bayesian inference variable selection missing data mixed model Markov chain Monte Carlo
2013/6/14
A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of ma...
In a model of the form $Y=h(X_1,\ldots,X_d)$ where the goal is to estimate a parameter of the probability distribution of $Y$, we define new sensitivity indices which quantify the importance of each v...
Adaptive Metropolis-Hastings Sampling using Reversible Dependent Mixture Proposals
Ergodic convergence Markov Chain Monte Carlo Metropolis-within Gibbs composite sampling Multivariatet mixtures Simulated annealing Variational Approx-imation
2013/6/14
This article develops a general-purpose adaptive sampler that approximates the target density by a mixture of multivariate t densities. The adaptive sampler is based on reversible proposal distributio...
A Gaussian Process Emulator Approach for Rapid Contaminant Characterization with an Integrated Multizone-CFD Model
xBayesian Framework Gaussian Process Emulator Multizone Models Integrated Multizone-CFD CONTAM Rapid Source Localization and Characterization
2013/6/14
This paper explores a Gaussian process emulator based approach for rapid Bayesian inference of contaminant source location and characteristics in an indoor environment. In the pre-event detection stag...
Fourier methods for smooth distribution function estimation
Fourier analysis kernel distribution estimation mean integrated squared error optimal bandwidth sinc kernel
2013/6/14
In this paper we show how to use Fourier transform methods to analyze the asymptotic behavior of kernel distribution function estimators. Exact expressions for the mean integrated squared error in ter...
Affine Invariant Divergences associated with Composite Scores and its Applications
composite score divergence Bregman score H¨older score affine invariance
2013/6/14
In statistical analysis, measuring a score of predictive performance is an important task. In many scientific fields, appropriate scores were tailored to tackle the problems at hand. A proper score is...