4期

最新文章

• Approximate residual balancing: debiased inference of average treatment effects in high dimensions AI翻译
  作者：Athey, Susan; Imbens, Guido W.; Wager, Stefan
  作者单位：Stanford University
  摘要：There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on pretreatment variables. The unconfoundedness assumption is often more plausible if a large number of pretreatment variables are included in the analysis, but this can worsen the performance of standard approaches to treatment effect estimation. We develop a me...
• AdaPT: an interactive procedure for multiple testing with side information AI翻译
  作者：Lei, Lihua; Fithian, William
  作者单位：University of California System; University of California Berkeley
  摘要：We consider the problem of multiple-hypothesis testing with generic side information: for each hypothesis H-i we observe both a p-value p(i) and some predictor x(i) encoding contextual information about the hypothesis. For large-scale problems, adaptively focusing power on the more promising hypotheses (those more likely to yield discoveries) can lead to much more powerful multiple-testing procedures. We propose a general iterative framework for this problem, the adaptive p-value thresholding ...
• Estimated Wold representation and spectral-density-driven bootstrap for time series AI翻译
  作者：Krampe, Jonas; Kreiss, Jens-Peter; Paparoditis, Efstathios
  作者单位：Braunschweig University of Technology; University of Cyprus
  摘要：The second-order dependence structure of purely non-deterministic stationary processes is described by the coefficients of the famous Wold representation. These coefficients can be obtained by factorizing the spectral density of the process. This relationship together with some spectral density estimator is used to obtain consistent estimators of these coefficients. A spectral-density-driven bootstrap for time series is then developed which uses the entire sequence of estimated moving average ...
• Auxiliary gradient-based sampling algorithms AI翻译
  作者：Titsias, Michalis K.; Papaspiliopoulos, Omiros
  作者单位：Athens University of Economics & Business; ICREA; Pompeu Fabra University
  摘要：We introduce a new family of Markov chain Monte Carlo samplers that combine auxiliary variables, Gibbs sampling and Taylor expansions of the target density. Our approach permits the marginalization over the auxiliary variables, yielding marginal samplers, or the augmentation of the auxiliary variables, yielding auxiliary samplers. The well-known Metropolis-adjusted Langevin algorithm MALA and preconditioned Crank-Nicolson-Langevin algorithm pCNL are shown to be special cases. We prove that mar...
• Bayesian inference for Gaussian graphical models beyond decomposable graphs AI翻译
  作者：Khare, Kshitij; Rajaratnam, Bala; Saha, Abhishek
  作者单位：State University System of Florida; University of Florida; University of California System; University of California Davis; University of California System; University of California Davis
  摘要：Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that, when the graph G is decomposable, Bayesian inference is significantly more tractable than in the general non-decomposable setting. Penalized likelihood inference in contrast has made tremendous gains in the past few years in terms of scalability and tractability. Bayesian inference, however, has not had the same level of success, though a scalable Bayesian approach has ...
• Semiparametrically efficient estimation in quantile regression of secondary analysis AI翻译
  作者：Liang, Liang; Ma, Yanyuan; Wei, Ying; Carroll, Raymond J.
  作者单位：Texas A&M University System; Texas A&M University College Station; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Columbia University; University of Technology Sydney
  摘要：Analysing secondary outcomes is a common practice for case-control studies. Traditional secondary analysis employs either completely parametric models or conditional mean regression models to link the secondary outcome to covariates. In many situations, quantile regression models complement mean-based analyses and provide alternative new insights on the associations of interest. For example, biomedical outcomes are often highly asymmetric, and median regression is more useful in describing the...
• Modelling non-stationary multivariate time series of counts via common factors AI翻译
  作者：Wang, Fangfang; Wang, Haonan
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Colorado State University System; Colorado State University Fort Collins
  摘要：We develop a new parameter-driven model for multivariate time series of counts. The time series is not necessarily stationary. We model the mean process as the product of modulating factors and unobserved stationary processes. The former characterizes the long-run movement in the data, whereas the latter is responsible for rapid fluctuations and other unknown or unavailable covariates. The unobserved stationary processes evolve independently of the past observed counts and might interact with ...
• Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects AI翻译
  作者：Shi, Chengchun; Song, Rui; Lu, Wenbin; Fu, Bo
  作者单位：North Carolina State University; Fudan University
  摘要：A salient feature of data from clinical trials and medical studies is inhomogeneity. Patients not only differ in baseline characteristics, but also in the way that they respond to treatment. Optimal individualized treatment regimes are developed to select effective treatments based on patient's heterogeneity. However, the optimal treatment regime might also vary for patients across different subgroups. We mainly consider patients' heterogeneity caused by groupwise individualized treatment effe...
• Confidence intervals for causal effects with invalid instruments by using two-stage hard thresholding with voting AI翻译
  作者：Guo, Zijian; Kang, Hyunseung; Cai, T. Tony; Small, Dylan S.
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of Wisconsin System; University of Wisconsin Madison; University of Pennsylvania
  摘要：A major challenge in instrumental variable (IV) analysis is to find instruments that are valid, or have no direct effect on the outcome and are ignorable. Typically one is unsure whether all of the putative IVs are in fact valid. We propose a general inference procedure in the presence of invalid IVs, called two-stage hard thresholding with voting. The procedure uses two hard thresholding steps to select strong instruments and to generate candidate sets of valid IVs. Voting takes the candidate...
• Asymptotic properties and information criteria for misspecified generalized linear mixed models AI翻译
  作者：Yu, Dalei; Zhang, Xinyu; Yau, Kelvin K. W.
  作者单位：Yunnan University of Finance & Economics; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; City University of Hong Kong
  摘要：The problem of misspecification poses challenges in model selection. The paper studies the asymptotic properties of estimators for generalized linear mixed models with misspecification under the framework of conditional Kullback-Leibler divergence. A conditional generalized information criterion is introduced, and a model selection procedure is proposed by minimizing the criterion. We prove that the model selection procedure proposed is asymptotically loss efficient when all the candidate mode...