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Fast inference in generalized linear models via expected log-likelihoods
Fast inference generalized linear models expected log-likelihoods
2013/6/14
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate comp...
Bayesian semiparametric analysis for two-phase studies of gene-environment interaction
Biased sampling colorectal cancer Dirichlet prior exposure enriched sampling gene-environment independence jointeffects multivariate categorical distribution spike and slab prior
2013/6/14
The two-phase sampling design is a cost-efficient way of collecting expensive covariate information on a judiciously selected subsample. It is natural to apply such a strategy for collecting genetic d...
Estimating treatment effect heterogeneity in randomized program evaluation
Causal inference individualized treatment rules LASSO moderation variable selection
2013/6/14
When evaluating the efficacy of social programs and medical treatments using randomized experiments, the estimated overall average causal effect alone is often of limited value and the researchers mus...
A general approach to the joint asymptotic analysis of statistics from sub-samples
Empirical processes sub-sampling,self-normalization change point weak con-vergence Time series compact differentiability
2013/6/14
In time series analysis, statistics based on collections of estimators computed from sub-samples play a crucial role in an increasing variety of important applications. Proving results about the joint...
A Primal Condition for Approachability with Partial Monitoring
Primal Condition Approachability with Partial Monitoring
2013/6/17
In approachability with full monitoring there are two types of conditions that are known to be equivalent for convex sets: a primal and a dual condition. The primal one is of the form: a set C is appr...
A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation
ASupervised Neural Autoregressive Topic Model Simultaneous Image Classification Annotation
2013/6/17
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation. Recently, a new type of topic model called the Document Neural Aut...
Matching on-the-fly in Sequential Experiments for Higher Power and Efficiency
Matching on-the-fly in Sequential Experiments Higher Power Efficiency
2013/6/14
We propose a dynamic allocation procedure that increases power and efficiency when measuring an average treatment effect in sequential randomized trials. Subjects arrive iteratively and are either ran...
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...