516期

最新文章

• Bayesian Nonparametric Modeling of Higher Order Markov Chains AI翻译
  作者：Sarkar, Abhra; Dunson, David B.
  作者单位：Duke University
  摘要：We consider the problem of flexible modeling of higher order Markov chains when an upper bound on the order of the chain is known but the true order and nature of the serial dependence are unknown. We propose Bayesian nonparametric methodology based on conditional tensor factorizations which can characterize any transition probability with a specified maximal order. The methodology selects the important lags and captures higher order interactions among the lags, while also facilitating calcula...
• Sparse Approximate Inference for Spatio-Temporal Point Process Models AI翻译
  作者：Cseke, Botond; Zammit-Mangion, Andrew; Heskes, Tom; Sanguinetti, Guido
  作者单位：University of Edinburgh; University of Bristol; Radboud University Nijmegen
  摘要：Spatio-temporal log-Gaussian Cox process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computationally challenging both due to the high-resolution modeling generally required and the analytically intractable likelihood function. Here, we exploit the sparsity structure typical of (spatially) discretized log-Gaussian Cox process models by using approximate message-passing algorithms. The proposed algorithms sca...
• A Link Between the E-Value and the Robustness of Block Designs AI翻译
  作者：Godolphin, J. D.
  作者单位：University of Surrey
  摘要：This article investigates the robustness of binary incomplete block designs against giving rise to a disconnected design in the event of observation loss. A link is established between the E-value of a planned design and the extent of observation loss that can be experienced while still guaranteeing an eventual design from which all treatment contrasts can be estimated. Patterns of missing observations covered include loss of entire blocks and loss of individual observations. Simple bounds are...
• Smoothing Parameter and Model Selection for General Smooth Models Comment AI翻译
  作者：Greven, Sonja; Scheipl, Fabian
  作者单位：University of Munich
• Hierarchical Feature Selection Incorporating Known and Novel Biological Information: Identifying Genomic Features Related to Prostate Cancer Recurrence AI翻译
  作者：Zhao, Yize; Chung, Matthias; Johnson, Brent A.; Moreno, Carlos S.; Long, Qi
  作者单位：Cornell University; Virginia Polytechnic Institute & State University; University of Rochester; Emory University; Emory University
  摘要：Our work is motivated by a prostate cancer study aimed at identifying mRNA and miRNA biomarkers that are predictive of cancer recurrence after prostatectomy. It has been shown in the literature that incorporating known biological information on pathway memberships and interactions among biomarkers improves feature selection of high-dimensional biomarkers in relation to disease risk. Biological information is often represented by graphs or networks, in which biomarkers are represented by nodes ...
• Smoothing Parameter and Model Selection for General Smooth Models AI翻译
  作者：Wood, Simon N.; Pya, Natalya; Saefken, Benjamin
  作者单位：University of Bristol; Nazarbayev University; KIMEP University; University of Gottingen; University of Gottingen
  摘要：This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of model selection tool...
• Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering AI翻译
  作者：Coretto, Pietro; Hennig, Christian
  作者单位：University of Salerno; University of London; University College London
  摘要：The two main topics of this article are the introduction of the optimally tuned robust improper maximum likelihood estimator (OTRIMLE) for robust clustering based on the multivariate Gaussian model for clusters, and a comprehensive simulation study comparing the OTRIMLE to maximum likelihood in Gaussian mixtures with and without noise component, mixtures oft-distributions, and the TCLUST approach for trimmed clustering. The OTRIMLE uses an improper constant density for modeling outliers and no...
• Composite Robust Estimators for Linear Mixed Models AI翻译
  作者：Agostinelli, Claudio; Yohai, Victor J.
  作者单位：University of Trento; Universita Ca Foscari Venezia; University of Buenos Aires
  摘要：The classical Tukey-Huber contamination model (CCM) is a commonly adopted framework to describe the mechanism of outliers generation in robust statistics. Given a dataset with n observations and p variables, under the CCM, an outlier is a unit, even if only one or a few values are corrupted. Classical robust procedures were designed to cope with this type of outliers. Recently, anew mechanism of outlier generation was introduced, namely, the independent contamination model (ICM), where the occ...
• Extremal Depth for Functional Data and Applications AI翻译
  作者：Narisetty, Naveen N.; Nair, Vijayan N.
  作者单位：University of Michigan System; University of Michigan
  摘要：We propose a new notion called extremal depth (ED) for functional data, discuss its properties, and compare its performance with existing concepts. The proposed notion is based on a measure of extreme outlyingness!' ED has several desirable properties that are not shared by other notions and is especially well suited for obtaining central regions of functional data and function spaces. In particular: (a) the central region achieves the nominal (desired) simultaneous coverage probability; (b) t...