2020

最新文章

• Panel Data Analysis via Mechanistic Models AI翻译
  作者：Breto, Carles; Ionides, Edward L.; King, Aaron A.
  作者单位：University of Michigan System; University of Michigan; University of Valencia; University of Michigan System; University of Michigan
  摘要：Panel data, also known as longitudinal data, consist of a collection of time series. Each time series, which could itself be multivariate, comprises a sequence of measurements taken on a distinct unit. Mechanistic modeling involves writing down scientifically motivated equations describing the collection of dynamic systems giving rise to the observations on each unit. A defining characteristic of panel systems is that the dynamic interaction between units should be negligible. Panel models the...
• Doubly Robust Inference With Nonprobability Survey Samples AI翻译
  作者：Chen, Yilin; Li, Pengfei; Wu, Changbao
  作者单位：University of Waterloo
  摘要：We establish a general framework for statistical inferences with nonprobability survey samples when relevant auxiliary information is available from a probability survey sample. We develop a rigorous procedure for estimating the propensity scores for units in the nonprobability sample, and construct doubly robust estimators for the finite population mean. Variance estimation is discussed under the proposed framework. Results from simulation studies show the robustness and the efficiency of our...
• Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis AI翻译
  作者：Zhang, Jingfei; Sun, Will Wei; Li, Lexin
  作者单位：University of Miami; Purdue University System; Purdue University; University of California System; University of California Berkeley
  摘要：Time-varying networks are fast emerging in a wide range of scientific and business applications. Most existing dynamic network models are limited to a single-subject and discrete-time setting. In this article, we propose a mixed-effect network model that characterizes the continuous time-varying behavior of the network at the population level, meanwhile taking into account both the individual subject variability as well as the prior module information. We develop a multistep optimization proce...
• Likelihood Ratio Tests for a Large Directed Acyclic Graph AI翻译
  作者：Li, Chunlin; Shen, Xiaotong; Pan, Wei
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Inference of directional pairwise relations between interacting units in a directed acyclic graph (DAG), such as a regulatory gene network, is common in practice, imposing challenges because of lack of inferential tools. For example, inferring a specific gene pathway of a regulatory gene network is biologically important. Yet, frequentist inference of directionality of connections remains largely unexplored for regulatory models. In this article, we propose constrained likelihood ratio tests f...
• Demand Models With Random Partitions AI翻译
  作者：Smith, Adam N.; Allenby, Greg M.
  作者单位：University of London; University College London; University System of Ohio; Ohio State University
  摘要：Many economic models of consumer demand require researchers to partition sets of products or attributes prior to the analysis. These models are common in applied problems when the product space is large or spans multiple categories. While the partition is traditionally fixed a priori, we let the partition be a model parameter and propose a Bayesian method for inference. The challenge is that demand systems are commonly multivariate models that are not conditionally conjugate with respect to pa...
• Ensemble Kalman Methods for High-Dimensional Hierarchical Dynamic Space-Time Models AI翻译
  作者：Katzfuss, Matthias; Stroud, Jonathan R.; Wikle, Christopher K.
  作者单位：Texas A&M University System; Texas A&M University College Station; Georgetown University; University of Missouri System; University of Missouri Columbia
  摘要：We propose a new class of filtering and smoothing methods for inference in high-dimensional, nonlinear, non-Gaussian, spatio-temporal state-space models. The main idea is to combine the ensemble Kalman filter and smoother, developed in the geophysics literature, with state-space algorithms from the statistics literature. Our algorithms address a variety of estimation scenarios, including online and off-line state and parameter estimation. We take a Bayesian perspective, for which the goal is t...
• A Hierarchical Model of Nonhomogeneous Poisson Processes for Twitter Retweets AI翻译
  作者：Lee, Clement; Wilkinson, Darren J.
  作者单位：Newcastle University - UK; Newcastle University - UK; Alan Turing Institute
  摘要：We present a hierarchical model of nonhomogeneous Poisson processes (NHPP) for information diffusion on online social media, in particular Twitter retweets. The retweets of each original tweet are modelled by a NHPP, for which the intensity function is a product of time-decaying components and another component that depends on the follower count of the original tweet author. The latter allows us to explain or predict the ultimate retweet count by a network centrality-related covariate. The inf...
• ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing for Aberrant Gene Expression Patterns AI翻译
  作者：Wilson, Douglas R.; Jin, Chong; Ibrahim, Joseph G.; Sun, Wei
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
  摘要：Immunotherapies have attracted lots of research interests recently. The need to understand the underlying mechanisms of immunotherapies and to develop precision immunotherapy regimens has spurred great interest in characterizing immune cell composition within the tumor microenvironment. Several methods have been developed to estimate immune cell composition using gene expression data from bulk tumor samples. However, these methods are not flexible enough to handle aberrant patterns of gene exp...
• Distribution on Warp Maps for Alignment of Open and Closed Curves AI翻译
  作者：Bharath, Karthik; Kurtek, Sebastian
  作者单位：University of Nottingham; University System of Ohio; Ohio State University
  摘要：Alignment of curve data is an integral part of their statistical analysis, and can be achieved using model- or optimization-based approaches. The parameter space is usually the set of monotone, continuous warp maps of a domain. Infinite-dimensional nature of the parameter space encourages sampling based approaches, which require a distribution on the set of warp maps. Moreover, the distribution should also enable sampling in the presence of important landmark information on the curves which co...
• A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis AI翻译
  作者：Mejia, Amanda F.; Yue, Yu (Ryan); Bolin, David; Lindgren, Finn; Lindquist, Martin A.
  作者单位：Indiana University System; Indiana University Bloomington; City University of New York (CUNY) System; Baruch College (CUNY); Chalmers University of Technology; University of Gothenburg; University of Edinburgh; Johns Hopkins University
  摘要：Cortical surface functional magnetic resonance imaging (cs-fMRI) has recently grown in popularity versus traditional volumetric fMRI. In addition to offering better whole-brain visualization, dimension reduction, removal of extraneous tissue types, and improved alignment of cortical areas across subjects, it is also more compatible with common assumptions of Bayesian spatial models. However, as no spatial Bayesian model has been proposed for cs-fMRI data, most analyses continue to employ the c...