512期

最新文章

• Bayesian Phylogenetic Inference Using a Combinatorial Sequential Monte Carlo Method AI翻译
  作者：Wang, Liangliang; Bouchard-Cote, Alexandre; Doucet, Arnaud
  作者单位：Simon Fraser University; University of British Columbia; University of Oxford
  摘要：The application of Bayesian methods to large-scale phylogenetics problems is increasingly limited by computational issues, motivating the development of methods that can complement existing Markov chain Monte Carlo (MCMC) schemes. Sequential Monte Carlo (SMC) methods are approximate inference algorithms that have become very popular for time series models. Such methods have been recently developed to address phylogenetic inference problems but currently available techniques are only applicable...
• High-Dimensional Variable Selection With Reciprocal L1-Regularization AI翻译
  作者：Song, Qifan; Liang, Faming
  作者单位：Purdue University System; Purdue University; State University System of Florida; University of Florida
  摘要：During the past decade, penalized likelihood methods have been widely used in variable selection problems, where the penalty functions are typically symmetric about 0, continuous and nondecreasing in (0, infinity). We propose a new penalized likelihood method, reciprocal Lasso (or in short, rLasso), based on a new class of penalty functions that are decreasing in (0,infinity), discontinuous at 0, and converge to infinity when the coefficients approach zero. The new penalty functions give nearl...
• An Adaptive Resampling Test for Detecting the Presence of Significant Predictors Comment AI翻译
  作者：Leeb, Hannes
  作者单位：University of Vienna
• Robust Filtering AI翻译
  作者：Calvet, Laurent E.; Czellar, Veronika; Ronchetti, Elvezio
  作者单位：Hautes Etudes Commerciales (HEC) Paris; emlyon business school; University of Geneva; University of Geneva
  摘要：Filtering methods are powerful tools to estimate the hidden state of a state-space model from observations available in real time. However, they are known to be highly sensitive to the presence of small misspecifications of the underlying model and to outliers in the observation process. In this article, we show that the methodology of robust statistics can be adapted to sequential filtering. We define a filter as being robust if the relative error in the state distribution caused by misspecif...
• Small Area Estimation With Uncertain Random Effects AI翻译
  作者：Datta, Gauri Sankar; Mandal, Abhyuday
  作者单位：University System of Georgia; University of Georgia
  摘要：Random effects models play an important role in model-based small area estimation. Random effects account for any lack of fit of a regression model for the population means of small areas on a set of explanatory variables. In a recent article, Datta, Hall, and Mandal showed that if the random effects can be dispensed with via a suitable test, then the model parameters and the small area means may be estimated with substantially higher accuracy. The work of Datta, Hall, and Mandal is most usefu...
• Bayesian Compressed Regression AI翻译
  作者：Guhaniyogi, Rajarshi; Dunson, David B.
  作者单位：University of California System; University of California Santa Cruz; Duke University
  摘要：As an alternative to variable selection or shrinkage in high-dimensional regression, we propose to randomly compress the predictors prior to analysis. This dramatically reduces storage and computational bottlenecks, performing well when the predictors can be projected to a low-dimensional linear subspace with minimal loss of information about the response. As opposed to existing Bayesian dimensionality reduction approaches, the exact posterior distribution conditional on the compressed data is...
• Untitled AI翻译
  作者：Lemieux, Thomas
  作者单位：University of British Columbia
• Latent Surface Models for Networks Using Aggregated Relational Data AI翻译
  作者：McCormick, Tyler H.; Zheng, Tian
  作者单位：University of Washington; University of Washington Seattle; Columbia University
  摘要：Despite increased interest across a range of scientific applications in modeling and understanding social network structure, collecting complete network data remains logistically and financially challenging, especially in the social sciences. This article introduces a latent surface representation of social network structure for partially observed network data. We derive a multivariate measure of expected (latent) distance between an observed actor and unobserved actors with given features. We...
• Semiparametric Pseudo-Likelihoods in Generalized Linear Models With Nonignorable Missing Data AI翻译
  作者：Zhao, Jiwei; Shao, Jun
  作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY; East China Normal University; University of Wisconsin System; University of Wisconsin Madison
  摘要：We consider identifiability and estimation in a generalized linear model in which the response variable and some covariates have missing values and the missing data mechanism is nonignorable and unspecified. We adopt a pseudo-likelihood approach that makes use of an instrumental variable to help identifying unknown parameters in the presence of nonignorable missing data. Explicit conditions for the identifiability of parameters are given. Some asymptotic properties of the parameter estimators ...
• Self-Normalization for Time Series: A Review of Recent Developments AI翻译
  作者：Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：This article reviews some recent developments on the inference of time series data using the self-normalized approach. We aim to provide a detailed discussion about the use of self-normalization in different contexts and highlight distinctive feature associated with each problem and connections among these recent developments. The topics covered include: confidence interval construction for a parameter in a weakly dependent stationary time series setting, change point detection in the mean, ro...