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作者：Taeb, Armeen; Shah, Parikshit; Chandrasekaran, Venkat
作者单位：California Institute of Technology; Yahoo! Inc
摘要：Models specified by low rank matrices are ubiquitous in contemporary applications. In many of these problem domains, the row-column space structure of a low rank matrix carries information about some underlying phenomenon, and it is of interest in inferential settings to evaluate the extent to which the row-column spaces of an estimated low rank matrix signify discoveries about the phenomenon. However, in contrast with variable selection, we lack a formal framework to assess true or false disc...
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作者：Todeschini, Adrien; Miscouridou, Xenia; Caron, Francois
作者单位：Centre National de la Recherche Scientifique (CNRS); Inria; Universite de Bordeaux; University of Oxford
摘要：We propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process and naturally generalizes existing probabilistic models with overlapping block structure to the sparse regime. Our construction builds on vectors of completely random measures and has interpretable parameters, each node being assigned a vector representing its levels of affiliation to some latent communities. We develop met...
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作者：Pollock, Murray; Fearnhead, Paul; Johansen, Adam M.; Roberts, Gareth O.
作者单位：University of Warwick; Lancaster University
摘要：This paper introduces a class of Monte Carlo algorithms which are based on the simulation of a Markov process whose quasi-stationary distribution coincides with a distribution of interest. This differs fundamentally from, say, current Markov chain Monte Carlo methods which simulate a Markov chain whose stationary distribution is the target. We show how to approximate distributions of interest by carefully combining sequential Monte Carlo methods with methodology for the exact simulation of dif...
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作者：Christensen, Jonathan; Ma, Li
作者单位：Duke University
摘要：Bayesian hierarchical models are used to share information between related samples and to obtain more accurate estimates of sample level parameters, common structure and variation between samples. When the parameter of interest is the distribution or density of a continuous variable, a hierarchical model for continuous distributions is required. Various such models have been described in the literature using extensions of the Dirichlet process and related processes, typically as a distribution...
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作者：Singh, Sarjinder
作者单位：Texas A&M University System; Texas A&M University Kingsville
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作者：Gataric, Milana; Wang, Tengyao; Samworth, Richard J.
作者单位：University of Cambridge
摘要：We introduce a new method for sparse principal component analysis, based on the aggregation of eigenvector information from carefully selected axis-aligned random projections of the sample covariance matrix. Unlike most alternative approaches, our algorithm is non-iterative, so it is not vulnerable to a bad choice of initialization. We provide theoretical guarantees under which our principal subspace estimator can attain the minimax optimal rate of convergence in polynomial time. In addition, ...
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作者：Cai, T. Tony; Guo, Zijian
作者单位：University of Pennsylvania; Rutgers University System; Rutgers University New Brunswick
摘要：The paper considers statistical inference for the explained variance beta T sigma beta under the high dimensional linear model Y=X beta+epsilon in the semisupervised setting, where beta is the regression vector and sigma is the design covariance matrix. A calibrated estimator, which efficiently integrates both labelled and unlabelled data, is proposed. It is shown that the estimator achieves the minimax optimal rate of convergence in the general semisupervised framework. The optimality result ...
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作者：Cinelli, Carlos; Hazlett, Chad
作者单位：University of California System; University of California Los Angeles
摘要：We extend the omitted variable bias framework with a suite of tools for sensitivity analysis in regression models that does not require assumptions on the functional form of the treatment assignment mechanism nor on the distribution of the unobserved confounders, naturally handles multiple confounders, possibly acting non-linearly, exploits expert knowledge to bound sensitivity parameters and can be easily computed by using only standard regression results. In particular, we introduce two nove...
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作者：Tang, Yanbo; Reid, Nancy
作者单位：University of Toronto; Vector Institute for Artificial Intelligence
摘要：We examine a higher order approximation to the significance function with increasing numbers of nuisance parameters, based on the normal approximation to an adjusted log-likelihood root. We show that the rate of the correction for nuisance parameters is larger than the correction for non-normality, when the parameter dimensionpisO(n(alpha)) for alpha<12. We specialize the results to linear exponential families and location-scale families and illustrate these with simulations.
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作者：Frazier, David T.; Robert, Christian P.; Rousseau, Judith
作者单位：Monash University; Universite PSL; Universite Paris-Dauphine; University of Warwick; University of Oxford
摘要：We analyse the behaviour of approximate Bayesian computation (ABC) when the model generating the simulated data differs from the actual data-generating process, i.e. when the data simulator in ABC is misspecified. We demonstrate both theoretically and in simple, but practically relevant, examples that when the model is misspecified different versions of ABC can yield substantially different results. Our theoretical results demonstrate that even though the model is misspecified, under regularit...
