Journal of the American Statistical Association

最新文章

• Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations AI翻译
  作者：Conrad, Patrick R.; Marzouk, Youssef M.; Pillai, Natesh S.; Smith, Aaron
  作者单位：Massachusetts Institute of Technology (MIT); Harvard University; University of Ottawa
  摘要：We construct a new framework for accelerating Markov chain Monte Carlo in posterior sampling problems where standard methods are limited by the computational cost of the likelihood, or of numerical models embedded therein. Our approach introduces local approximations of these models into the Metropolis Hastings kernel, borrowing ideas from deterministic approximation theory, optimization, and experimental design. Previous efforts at integrating approximate models into inference typically sacri...
• Improving and Evaluating Topic Models and Other Models of Text Comment AI翻译
  作者：Goeva, Aleksandrina; Kolaczyk, Eric D.
  作者单位：Boston University
• Model Comparison and Assessment for Single Particle Tracking in Biological Fluids AI翻译
  作者：Lysy, Martin; Pillai, Natesh S.; Hill, David B.; Forest, M. Gregory; Mellnik, John W. R.; Vasquez, Paula A.; McKinley, Scott A.
  作者单位：University of Waterloo; Harvard University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of South Carolina System; University of South Carolina Columbia; Tulane University
  摘要：State-of-the-art techniques in passive particle-tracking microscopy provide high-resolution path trajectories of diverse foreign particles in biological fluids. For particles on the order of 1 mu m diameter, these paths are generally inconsistent with simple Brownian motion. Yet, despite an abundance of data confirming these findings and their wide-ranging scientific implications, stochastic modeling of the complex particle motion has received comparatively little attention. Even among posited...
• Smoothing Parameter and Model Selection for General Smooth Models Comment AI翻译
  作者：Yee, Thomas W.
  作者单位：University of Auckland
• Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times Comment AI翻译
  作者：Trippa, Lorenzo; Parmigiani, Giovanni
  作者单位：Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Harvard T.H. Chan School of Public Health
• Active Clinical Trials for Personalized Medicine AI翻译
  作者：Minsker, Stanislav; Zhao, Ying-Qi; Cheng, Guang
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：Individualized treatment rules (ITRs) tailor treatments according to individual patient characteristics. They can significantly improve patient care and are thus becoming increasingly popular. The data collected during randomized clinical trials are often used to estimate the optimal ITRs. However, these trials are generally expensive to run, and, moreover, they are not designed-to efficiently estimate ITRs. In this article, we propose a cost-effective estimation method from an active learning...
• A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making AI翻译
  作者：Zhou, Bo; Moorman, David E.; Behseta, Sam; Ombao, Hernando; Shahbaba, Babak
  作者单位：University of California System; University of California Irvine
  摘要：The goal of this article is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision-making. For an individual to successfully complete the task of decision-making, a number of temporally organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case th...
• Improving and Evaluating Topic Models and Other Models of Text Comment AI翻译
  作者：Blei, David M.
  作者单位：Columbia University; Columbia University
• Personalized Dose Finding Using Outcome Weighted Learning AI翻译
  作者：Chen, Guanhua; Zeng, Donglin; Kosorok, Michael R.
  作者单位：Vanderbilt University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：In dose-finding clinical trials, it is becoming increasingly important to account for individual-level heterogeneity while searching for optimal doses to ensure an optimal individualized dose rule (IDR) maximizes the expected beneficial clinical outcome for each individual. In this article, we advocate a randomized trial design where candidate dose levels assigned to study subjects are randomly chosen from a continuous distribution within a safe range. To estimate the optimal IDR using such da...
• Model-Based Geostatistics for Prevalence Mapping in Low-Resource Settings Rejoinder AI翻译
  作者：Diggle, Peter J.; Giorgi, Emanuele
  作者单位：Lancaster University