522期

最新文章

• Efficient Estimation for Semiparametric Structural Equation Models With Censored Data AI翻译
  作者：Wong, Kin Yau; Zeng, Donglin; Lin, D. Y.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：Structural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. We propose a general class of structural equation models with a semiparametric component for potentially censored survival times. We consider nonparametric maximum likelihood estimation and devise a combined expectation-maximization and Newton-Raphson algorithm for its implementation. We establish conditions for model identifiability and prove the con...
• Unsupervised Self-Normalized Change-Point Testing for Time Series AI翻译
  作者：Zhang, Ting; Lavitas, Liliya
  作者单位：Boston University
  摘要：We propose a new self-normalized method for testing change points in the time series setting. Self-normalization has been celebrated for its ability to avoid direct estimation of the nuisance asymptotic variance and its flexibility of being generalized to handle quantities other than the mean. However, it was developed and mainly studied for constructing confidence intervals for quantities associated with a stationary time series, and its adaptation to change-point testing can be nontrivial as...
• Bayesian Nonparametric Calibration and Combination of Predictive Distributions AI翻译
  作者：Bassetti, Federico; Casarin, Roberto; Ravazzolo, Francesco
  作者单位：University of Pavia; Universita Ca Foscari Venezia; Free University of Bozen-Bolzano
  摘要：We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting, we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined...
• Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes AI翻译
  作者：Swanson, Sonja A.; Hernan, Miguel A.; Miller, Matthew; Robins, James M.; Richardson, Thomas S.
  作者单位：Erasmus University Rotterdam; Erasmus MC; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Northeastern University; University of Washington; University of Washington Seattle
  摘要：Several methods have been proposed for partially or point identifying the average treatment effect (ATE) using instrumental variable (IV) type assumptions. The descriptions of these methods are widespread across the statistical, economic, epidemiologic, and computer science literature, and the connections between the methods have not been readily apparent. In the setting of a binary instrument, treatment, and outcome, we review proposed methods for partial and point identification of the ATE u...
• Estimating the Number of Sources in Magnetoencephalography Using Spiked Population Eigenvalues AI翻译
  作者：Yao, Zhigang; Zhang, Ye; Bai, Zhidong; Eddy, William F.
  作者单位：National University of Singapore; Orebro University; Northeast Normal University - China; Northeast Normal University - China; Carnegie Mellon University
  摘要：Magnetoencephalography (MEG) is an advanced imaging technique used to measure the magnetic fields outside the human head produced by the electrical activity inside the brain. Various source localization methods in MEG require the knowledge of the underlying active sources, which are identified by a priori. Common methods used to estimate the number of sources include principal component analysis or information criterion methods, both of which make use of the eigenvalue distribution of the data...
• 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...
• 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...