547期

最新文章

• Bahadur Efficiency of Observational Block Designs AI翻译
  作者：Rosenbaum, Paul R.
  作者单位：University of Pennsylvania
  摘要：To be convincing, an observational or nonrandomized study of causal effects must demonstrate that its conclusions cannot be readily explained by a small unmeasured bias in the way individuals were assigned to treatment or control. The Bahadur relative efficiency of a sensitivity analysis compares the performance of different test statistics or different research designs when sensitivity to unmeasured bias is appraised: better statistics and better designs exhibit insensitivity to larger biases...
• Multivariate Sparse Clustering for Extremes AI翻译
  作者：Meyer, Nicolas; Wintenberger, Olivier
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite de Montpellier; Inria; Sorbonne Universite; Universite Paris Cite; University of Vienna
  摘要：Identifying directions where extreme events occur is a significant challenge in multivariate extreme value analysis. In this article, we use the concept of sparse regular variation introduced by Meyer and Wintenberger to infer the tail dependence of a random vector X. This approach relies on the Euclidean projection onto the simplex which better exhibits the sparsity structure of the tail of X than the standard methods. Our procedure based on a rigorous methodology aims at capturing clusters o...
• Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Models AI翻译
  作者：Wang, Yixin; Degleris, Anthony; Williams, Alex; Linderman, Scott W.
  作者单位：University of Michigan System; University of Michigan; Stanford University; New York University; Simons Foundation; Flatiron Institute; Stanford University; Stanford University
  摘要：Neyman-Scott processes (NSPs) are point process models that generate clusters of points in time or space. They are natural models for a wide range of phenomena, ranging from neural spike trains to document streams. The clustering property is achieved via a doubly stochastic formulation: first, a set of latent events is drawn from a Poisson process; then, each latent event generates a set of observed data points according to another Poisson process. This construction is similar to Bayesian nonp...
• On the Estimation of the Number of Communities for Sparse Networks AI翻译
  作者：Hwang, Neil; Xu, Jiarui; Chatterjee, Shirshendu; Bhattacharyya, Sharmodeep
  作者单位：City University of New York (CUNY) System; City University of New York (CUNY) System; Oregon State University
  摘要：Among the nonparametric methods of estimating the number of communities (K) in a community detection problem, methods based on the spectrum of the Bethe Hessian matrices (H-? with the scalar parameter ?) have garnered much popularity for their simplicity, computational efficiency, and robustness to the sparsity of data. For certain heuristic choices of ?, such methods have been shown to be consistent for networks with N nodes with a common expected degree of ?( log N). In this article, we obta...
• Ideal Bayesian Spatial Adaptation AI翻译
  作者：Rockova, Veronika; Rousseau, Judith
  作者单位：University of Chicago; University of Oxford; Universite PSL; Universite Paris-Dauphine
  摘要：Many real-life applications involve estimation of curves that exhibit complicated shapes including jumps or varying-frequency oscillations. Practical methods have been devised that can adapt to a locally varying complexity of an unknown function (e.g., variable-knot splines, sparse wavelet reconstructions, kernel methods or trees/forests). However, the overwhelming majority of existing asymptotic minimaxity theory is predicated on homogeneous smoothness assumptions. Focusing on locally H & oum...
• Robust Leave-One-Out Cross-Validation for High-Dimensional Bayesian Models AI翻译
  作者：Silva, Luca Alessandro; Zanella, Giacomo
  作者单位：Bocconi University; Bocconi University; Bocconi University
  摘要：Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to fit the model multiple times. In the Bayesian context, importance sampling provides a possible solution but classical approaches can easily produce estimators whose asymptotic variance is infinite, making them potentially unreliable. Here we propose and analyze a novel mixture estimator to compute Ba...
• Unified Unconditional Regression for Multivariate Quantiles, M-Quantiles, and Expectiles AI翻译
  作者：Merlo, Luca; Petrella, Lea; Salvati, Nicola; Tzavidis, Nikos
  作者单位：European University of Rome; Sapienza University Rome; University of Pisa; University of Southampton
  摘要：In this article, we develop a unified regression approach to model unconditional quantiles, M-quantiles and expectiles of multivariate dependent variables exploiting the multidimensional Huber's function. To assess the impact of changes in the covariates across the entire unconditional distribution of the responses, we extend the work of Firpo, Fortin, and Lemieux by running a mean regression of the recentered influence function on the explanatory variables. We discuss the estimation procedure...
• An Automated Approach to Causal Inference in Discrete Settings AI翻译
  作者：Duarte, Guilherme; Finkelstein, Noam; Knox, Dean; Mummolo, Jonathan; Shpitser, Ilya
  作者单位：University of Pennsylvania; Johns Hopkins University; Princeton University; Princeton University
  摘要：Applied research conditions often make it impossible to point-identify causal estimands without untenable assumptions. Partial identification-bounds on the range of possible solutions-is a principled alternative, but the difficulty of deriving bounds in idiosyncratic settings has restricted its application. We present a general, automated numerical approach to causal inference in discrete settings. We show causal questions with discrete data reduce to polynomial programming problems, then pres...
• Latent Multimodal Functional Graphical Model Estimation AI翻译
  作者：Tsai, Katherine; Zhao, Boxin; Koyejo, Sanmi; Kolar, Mladen
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Chicago; Stanford University
  摘要：Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences. One prominent motivation to acquire such data is to enable new discoveries of the underlying connectivity by combining multimodal signals. Despite the scientific interest, there remains a gap in principled statistical methods f...
• A Randomized Pairwise Likelihood Method for Complex Statistical Inferences AI翻译
  作者：Mazo, Gildas; Karlis, Dimitris; Rau, Andrea
  作者单位：INRAE; Universite Paris Saclay; Athens University of Economics & Business; INRAE; Universite Paris Saclay; AgroParisTech; Universite de Lille; Universite de Picardie Jules Verne (UPJV); INRAE
  摘要：Pairwise likelihood methods are commonly used for inference in parametric statistical models in cases where the full likelihood is too complex to be used, such as multivariate count data. Although pairwise likelihood methods represent a useful solution to perform inference for intractable likelihoods, several computational challenges remain. The pairwise likelihood function still requires the computation of a sum over all pairs of variables and all observations, which may be prohibitive in hig...