2018

最新文章

• Mixtures of g-Priors in Generalized Linear Models AI翻译
  作者：Li, Yingbo; Clyde, Merlise A.
  作者单位：Clemson University; Duke University
  摘要：Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to generalized linear models (GLMs) have been proposed in the literature; however, the choice of prior distribution of g and resulting properties for inference have received considerably less attention. In this article, we unify mixtures of g-priors in GLMs by assigning the tru...
• A Bayesian Phase I/II Trial Design for Immunotherapy AI翻译
  作者：Liu, Suyu; Guo, Beibei; Yuan, Ying
  作者单位：University of Texas System; UTMD Anderson Cancer Center; Louisiana State University System; Louisiana State University
  摘要：Immunotherapy is an innovative treatment approach that stimulates a patient's immune system to fight cancer. It demonstrates characteristics distinct from conventional chemotherapy and stands to revolutionize cancer treatment. We propose a Bayesian phase I/II dose-finding design that incorporates the unique features of immunotherapy by simultaneously considering three outcomes: immune response, toxicity, and efficacy. The objective is to identify the biologically optimal dose, defined as the d...
• Using Standard Tools From Finite Population Sampling to Improve Causal Inference for Complex Experiments AI翻译
  作者：Mukerjee, Rahul; Dasgupta, Tirthankar; Rubin, Donald B.
  作者单位：Indian Institute of Management (IIM System); Indian Institute of Management Calcutta; Harvard University
  摘要：This article considers causal inference for treatment contrasts from a randomized experiment using potential outcomes in a finite population setting. Adopting a Neymanian repeated sampling approach that integrates such causal inference with finite population survey sampling, an inferential framework is developed for general mechanisms of assigning experimental units to multiple treatments. This framework extends classical methods by allowing the possibility of randomization restrictions and un...
• A Powerful Bayesian Test for Equality of Means in High Dimensions AI翻译
  作者：Zoh, Roger S.; Sarkar, Abhra; Carroll, Raymond J.; Mallick, Bani K.
  作者单位：Texas A&M University System; Texas A&M University College Station; Duke University; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
  摘要：We develop a Bayes factor-based testing procedure for comparing two population means in high-dimensional settings. In large-p-small-n settings, Bayes factors based on proper priors require eliciting a large and complex p x p covariance matrix, whereas Bayes factors based on Jeffrey's prior suffer the same impediment as the classical Hotelling T-2 test statistic as they involve inversion of ill-formed sample covariance matrices. To circumvent this limitation, we propose that the Bayes factor be...
• The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method AI翻译
  作者：Bouchard-Cote, Alexandre; Vollmer, Sebastian J.; Doucet, Arnaud
  作者单位：University of British Columbia; University of Warwick; University of Warwick; Alan Turing Institute; University of Oxford
  摘要：Many Markov chain Monte Carlo techniques currently available rely on discrete-time reversible Markov processes whose transition kernels are variations of the Metropolis-Hastings algorithm. We explore and generalize an alternative scheme recently introduced in the physics literature (Peters and de With 2012) where the target distribution is explored using a continuous-time nonreversible piecewise-deterministic Markov process. In the Metropolis-Hastings algorithm, a trial move to a region of low...
• Optimal Control and Additive Perturbations Help in Estimating Ill-Posed and Uncertain Dynamical Systems AI翻译
  作者：Clairon, Quentin; Brunel, Nicolas J. -B.
  作者单位：Newcastle University - UK; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); Ecole Nationale Superieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE); Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay
  摘要：Ordinary differential equations (ODE) are routinely calibrated on real data for estimating unknown parameters or for reverse-engineering. Nevertheless, standard statistical techniques can give disappointing results because of the complex relationship between parameters and states, which makes the corresponding estimation problem ill-posed. Moreover, ODE are mechanistic models that are prone to modeling errors, whose influences on inference are often neglected during statistical analysis. We pr...
• Over-Dispersed Age-Period-Cohort Models AI翻译
  作者：Harnau, Jonas; Nielsen, Bent
  作者单位：University of Oxford
  摘要：We consider inference and forecasting for aggregate data organized in a two-way table with age and cohort as indices, but without measures of exposure. This is modeled using a Poisson likelihood with an age-period-cohort structure for the mean while allowing for over-dispersion. We propose a repetitive structure that keeps the dimension of the table fixed while increasing the latent exposure. For this, we use a class of infinitely divisible distributions which include a variety of compound Poi...
• Oracle Estimation of a Change Point in High-Dimensional Quantile Regression AI翻译
  作者：Lee, Sokbae; Liao, Yuan; Seo, Myung Hwan; Shin, Youngki
  作者单位：Columbia University; University of London; London School Economics & Political Science; Rutgers University System; Rutgers University New Brunswick; Seoul National University (SNU); University of Technology Sydney; McMaster University
  摘要：In this article, we consider a high-dimensional quantile regression model where the sparsity structure may differ between two sub-populations. We develop (1)-penalized estimators of both regression coefficients and the threshold parameter. Our penalized estimators not only select covariates but also discriminate between a model with homogenous sparsity and a model with a change point. As a result, it is not necessary to know or pretest whether the change point is present, or where it occurs. O...
• Bayesian Inference in the Presence of Intractable Normalizing Functions AI翻译
  作者：Park, Jaewoo; Haran, Murali
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Models with intractable normalizing functions arise frequently in statistics. Common examples of such models include exponential random graph models for social networks and Markov point processes for ecology and disease modeling. Inference for these models is complicated because the normalizing functions of their probability distributions include the parameters of interest. In Bayesian analysis, they result in so-called doubly intractable posterior distributions which pose significant computat...
• Variable Selection for Skewed Model-Based Clustering: Application to the Identification of Novel Sleep Phenotypes AI翻译
  作者：Wallace, Meredith L.; Buysse, Daniel J.; Germain, Anne; Hall, Martica H.; Iyengar, Satish
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：In sleep research, applying finite mixture models to sleep characteristics captured through multiple data types, including self-reported sleep diary, a wrist monitor capturing movement (actigraphy), and brain waves (polysomnography), may suggest new phenotypes that reflect underlying disease mechanisms. However, a direct mixture model application is challenging because there are many sleep variables from which to choose, and sleep variables are often highly skewed even in homogenous samples. M...