516期

最新文章

• Modeling the Evolution of Dynamic Brain Processes During an Associative Learning Experiment AI翻译
  作者：Fiecas, Mark; Ombao, Hernando
  作者单位：University of Warwick; University of California System; University of California Irvine
  摘要：We develop a new time series model to investigate the dynamic interactions between the nucleus accumbens and the hippocampus during an associative learning experiment. Preliminary analyses indicated that the spectral properties of the local field potentials at these two regions changed over the trials of the experiment. While many models already take into account nonstationarity within a single trial, the evolution of the dynamics across trials is often ignored. Our proposed model, the slowly ...
• Personalized Dose Finding Using Outcome Weighted Learning Comment AI翻译
  作者：Qian, Min
  作者单位：Columbia University
  摘要：This comment deals with issues related to the article by Chen, Zeng, and Kosorok. We present several potential modifications of the outcome weighted learning approach.Those modifications are basecIon truncated l(2) loss. One advantage of l(2) loss is that it is differentiable everywhere, which makes it more stable and computationally more tractable.
• Personalized Dose Finding Using Outcome Weighted Learning Comment AI翻译
  作者：Ogburn, Elizabeth L.
  作者单位：Johns Hopkins University
• Multitask Quantile Regression Under the Transnormal Model AI翻译
  作者：Fan, Jianqing; Xue, Lingzhou; Zou, Hui
  作者单位：Princeton University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Minnesota System; University of Minnesota Twin Cities
  摘要：We consider estimating multitask quantile regression under the transnormal model, with focus on high dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based l(1), penalization with positive-definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of the altern...
• Identifiability of Normal and Normal Mixture Models with Nonignorable Missing Data AI翻译
  作者：Miao, Wang; Ding, Peng; Geng, Zhi
  作者单位：Peking University; University of California System; University of California Berkeley; Peking University; Peking University
  摘要：Missing data problems arise in many applied research studies. They may jeopardize statistical inference of the model of interest, if the missing mechanism is nonignorable, that is, the missing mechanism depends on the missing values themselves even conditional on the observed data. With a nonignorable missing mechanism, the model of interest is often not identifiable without imposing further assumptions. We find that even if the missing mechanism has a known parametric form, the model is not i...
• Modeling Probability Forecasts via Information Diversity AI翻译
  作者：Satopaa, Ville A.; Pemantle, Robin; Ungar, Lyle H.
  作者单位：INSEAD Business School; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：Randomness in scientific estimation is generally assumed to arise from unmeasured or uncontrolled factors. However, when combining subjective probability estimates, heterogeneity stemming from people's cognitive or information diversity is often more important than measurement noise. This article presents a novel framework that uses partially overlapping information sources. A specific model is proposed within that framework and applied to the task of aggregating the probabilities given by a g...
• Personalized Dose Finding Using Outcome Weighted Learning Comment AI翻译
  作者：Cai, Tianxi; Tian, Lu
  作者单位：Harvard University; Stanford University
• Personalized Dose Finding Using Outcome Weighted Learning Comment AI翻译
  作者：Luedtke, Alexander R.; van der Laan, Mark J.
  作者单位：Fred Hutchinson Cancer Center; University of California System; University of California Berkeley
• Multiple Imputation of Missing Categorical and Continuous Values via Bayesian Mixture Models With Local Dependence AI翻译
  作者：Murray, Jared S.; Reiter, Jerome P.
  作者单位：Carnegie Mellon University; Duke University
  摘要：We present a nonparametric Bayesian joint model for multivariate continuous and categorical variables, with the intention of developing a flexible engine for multiple imputation of missing values. The model fuses Dirichlet process mixtures of multinomial distributions for categorical variables with Dirichlet process mixtures of multivariate normal distributions for continuous variables. We incorporate dependence between the continuous-and categorical variables by (1) modeling the means of the ...
• 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...