501期

最新文章

• On Sampling Strategies in Bayesian Variable Selection Problems With Large Model Spaces AI翻译
  作者：Garcia-Donato, G.; Martinez-Beneito, M. A.
  作者单位：Universidad de Castilla-La Mancha; CIBER - Centro de Investigacion Biomedica en Red; CIBERESP
  摘要：One important aspect of Bayesian model selection is how to deal with huge model spaces, since the exhaustive enumeration of all the models entertained is not feasible and inferences have to be based on the very small proportion of models visited. This is the case for the variable selection problem with a moderately large number of possible explanatory variables considered in this article. We review some of the strategies proposed in the literature, from a theoretical point of view using argume...
• Estimating Latent Processes on a Network From Indirect Measurements AI翻译
  作者：Airoldi, Edoardo M.; Blocker, Alexander W.
  作者单位：Harvard University; Harvard University
  摘要：In a communication network, point-to-point traffic volumes over time are critical for designing protocols that route information efficiently and for maintaining security, whether at the scale of an Internet service provider or within a corporation. While technically feasible, the direct measurement of point-to-point traffic imposes a heavy burden on network performance and is typically not implemented. Instead, indirect aggregate traffic volumes are routinely collected. We consider the problem...
• Misspecification Testing in a Class of Conditional Distributional Models AI翻译
  作者：Rothe, Christoph; Wied, Dominik
  作者单位：Columbia University; Dortmund University of Technology
  摘要：We propose a specification test for a wide range of parametric models for the conditional distribution function of an outcome variable given a vector of covariates. The test is based on the Cramer-von Mises distance between an unrestricted estimate of the joint distribution function of the data and a restricted estimate that imposes the structure implied by the model. The procedure is straightforward to implement, is consistent against fixed alternatives, has nontrivial power against local dev...
• A Bayesian Graphical Model for ChIP-Seq Data on Histone Modifications AI翻译
  作者：Mitra, Riten; Mueller, Peter; Liang, Shoudan; Yue, Lu; Ji, Yuan
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; NorthShore University Health System
  摘要：Histone modifications (HMs) are an important post-translational feature. Different types of HMs are believed to co-exist and co-regulate biological processes such as gene expression and, therefore, are intrinsically dependent on each other. We develop inference for this complex biological network of HMs based on a graphical model using ChIP-Seq data. A critical computational hurdle in the inference for the proposed graphical model is the evaluation of a normalization constant in an autologisti...
• Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter AI翻译
  作者：Ryu, Duchwan; Liang, Faming; Mallick, Bani K.
  作者单位：University System of Georgia; Augusta University; Texas A&M University System; Texas A&M University College Station
  摘要：The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle ...
• Imputation in High-Dimensional Economic Data as Applied to the Agricultural Resource Management Survey AI翻译
  作者：Robbins, Michael W.; Ghosh, Sujit K.; Habiger, Joshua D.
  作者单位：University of Missouri System; University of Missouri Columbia; North Carolina State University; Oklahoma State University System; Oklahoma State University - Stillwater
  摘要：In this article, we consider imputation in the USDA's Agricultural Resource Management Survey (ARMS) data, which is a complex, high-dimensional economic dataset. We develop a robust joint model for ARMS data, which requires that variables are transformed using a suitable class of marginal densities (e.g., skew normal family). We assume that the transformed variables may be linked through a Gaussian copula, which enables construction of the joint model via a sequence of conditional linear model...
• On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models AI翻译
  作者：Feng, Zhenghui; Wen, Xuerong Meggie; Yu, Zhou; Zhu, Lixing
  作者单位：Xiamen University; Xiamen University; University of Missouri System; Missouri University of Science & Technology; East China Normal University; Hong Kong Baptist University
  摘要：Partial dimension reduction is a general method to seek informative convex combinations of predictors of primary interest, which includes dimension reduction as its special case when the predictors in the remaining part are constants. In this article, we propose a novel method to conduct partial dimension reduction estimation for predictors of primary interest without assuming that the remaining predictors are categorical. To this end, we first take the dichotomization step such that any exist...
• Learning Sparse Causal Gaussian Networks With Experimental Intervention: Regularization and Coordinate Descent AI翻译
  作者：Fu, Fei; Zhou, Qing
  作者单位：University of California System; University of California Los Angeles
  摘要：Causal networks are graphically represented by directed acyclic graphs (DAGs). Learning causal networks from data is a challenging problem due to the size of the space of DAGs, the acyclicity constraint placed on the graphical structures, and the presence of equivalence classes. In this article, we develop an L-1-penalized likelihood approach to estimate the structure of causal Gaussian networks. A blockwise coordinate descent algorithm, which takes advantage of the acyclicity constraint, is p...
• A Bayesian Procedure for File Linking to Analyze End-of-Life Medical Costs AI翻译
  作者：Gutman, Roee; Afendulis, Christopher C.; Zaslavsky, Alan M.
  作者单位：Brown University; Harvard University; Harvard Medical School
  摘要：End-of-life medical expenses are a significant proportion of all health care expenditures. These costs were studied using costs of services from Medicare claims and cause of death (CoD) from death certificates. In the absence of a unique identifier linking the two datasets, common variables identified unique matches for only 33% of deaths. The remaining cases formed cells with multiple cases (32% in cells with an equal number of cases from each file and 35% in cells with an unequal number). We...
• Inferential Models: A Framework for Prior-Free Posterior Probabilistic Inference AI翻译
  作者：Martin, Ryan; Liu, Chuanhai
  作者单位：University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Purdue University System; Purdue University
  摘要：Posterior probabilistic statistical inference without priors is an important but so far elusive goal. Fisher's fiducial inference, Dempster-Shafer theory of belief functions, and Bayesian inference with default priors are attempts to achieve this goal but, to date, none has given a completely satisfactory picture. This article presents a new framework for probabilistic inference, based on inferential models (IMs), which not only provides data-dependent probabilistic measures of uncertainty abo...