Biometrika

最新文章

• Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes AI翻译
  作者：Zhang, Jeffrey; Small, Dylan S.; Heng, Siyu
  作者单位：University of Pennsylvania; New York University
  摘要：Matching is one of the most widely used study designs for adjusting for measured confounders in observational studies. However, unmeasured confounding may exist and cannot be removed by matching. Therefore, a sensitivity analysis is typically needed to assess a causal conclusion's sensitivity to unmeasured confounding. Sensitivity analysis frameworks for binary exposures have been well established for various matching designs and are commonly used in various studies. However, unlike the binary...
• An adaptive null proportion estimator for false discovery rate control AI翻译
  作者：Gao, Zijun
  作者单位：University of Southern California
  摘要：The false discovery rate is a commonly used criterion in multiple testing, and the Benjamini-Hochberg procedure is a standard approach to false discovery rate control. To increase its power, adaptive Benjamini-Hochberg procedures, that use estimates of the null proportion, have been proposed. A particularly popular approach being that based on Storey's estimator. The performance of Storey's estimator hinges on a critical hyperparameter, such that a pre-fixed configuration may lack power and ex...
• Regression analysis of group-tested current status data AI翻译
  作者：Li, Shuwei; Hu, Tao; Wang, Lianming; McMahan, Christopher S.; Tebbs, Joshua M.
  作者单位：Guangzhou University; Capital Normal University; University of South Carolina System; University of South Carolina Columbia; Clemson University; University of South Carolina System; University of South Carolina Columbia
  摘要：Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals are assessed only once and a time-to-event endpoint is recorded, for example, the time until infection. Combining group testing with this type of endpoint results in group-tested current status data (). To ...
• Individualized dynamic latent factor model for multi-resolutional data with application to mobile health AI翻译
  作者：Zhang, J.; Xue, F.; Xu, Q.; Lee, J.; Qu, A.
  作者单位：University of California System; University of California Irvine; Purdue University System; Purdue University; University of California System; University of California Irvine; University of California System; University of California Irvine
  摘要：Mobile health has emerged as a major success for tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data that arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled ...
• Likelihood-based inference under nonconvex boundary constraints AI翻译
  作者：Wang, J. Y.; Ye, Z. S.; Chen, Y.
  作者单位：National University of Singapore; University of Pennsylvania
  摘要：Likelihood-based inference under nonconvex constraints on model parameters has become increasingly common in biomedical research. In this paper, we establish large-sample properties of the maximum likelihood estimator when the true parameter value lies at the boundary of a nonconvex parameter space. We further derive the asymptotic distribution of the likelihood ratio test statistic under nonconvex constraints on model parameters. A general Monte Carlo procedure for generating the limiting dis...
• Bayesian learning of network structures from interventional experimental data AI翻译
  作者：Castelletti, F.; Peluso, S.
  作者单位：Catholic University of the Sacred Heart; University of Milano-Bicocca
  摘要：Directed acyclic graphs provide an effective framework for learning causal relationships among variables given multivariate observations. Under pure observational data, directed acyclic graphs encoding the same conditional independencies cannot be distinguished and are collected into Markov equivalence classes. In many contexts, however, observational measurements are supplemented by interventional data that improve directed acyclic graph identifiability and enhance causal effect estimation. W...
• On propensity score matching with a diverging number of matches AI翻译
  作者：He, Yihui; Han, Fang
  作者单位：Peking University; University of Washington; University of Washington Seattle
  摘要：This paper re-examines the work of on propensity score matching for average treatment effect estimation. We explore the asymptotic behaviour of these estimators when the number of nearest neighbours, M, grows with the sample size. It is shown, while not surprising, but technically nontrivial, that the modified estimators can improve upon the original fixed M-estimators in terms of efficiency. Additionally, we demonstrate the potential to attain the semiparametric efficiency lower bound when th...
• On varimax asymptotics in network models and spectral methods for dimensionality reduction AI翻译
  作者：Cape, J.
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：Varimax factor rotations, while popular among practitioners in psychology and statistics since being introduced by , have historically been viewed with skepticism and suspicion by some theoreticians and mathematical statisticians. Now, work by provides new, fundamental insight: varimax rotations provably perform statistical estimation in certain classes of latent variable models when paired with spectral-based matrix truncations for dimensionality reduction. We build on this new-found understa...
• Efficient evaluation of natural stochastic policies in off-line reinforcement learning AI翻译
  作者：Kallus, Nathan; Uehara, Masatoshi
  作者单位：Cornell University; Cornell University
  摘要：We study the efficient off-policy evaluation of natural stochastic policies, which are defined in terms of deviations from the unknown behaviour policy. This is a departure from the literature on off-policy evaluation that largely considers the evaluation of explicitly specified policies. Crucially, off-line reinforcement learning with natural stochastic policies can help alleviate issues of weak overlap, lead to policies that build upon current practice and improve policies' implementability ...
• Scalable subsampling: computation, aggregation and inference AI翻译
  作者：Politis, Dimitris
  作者单位：University of California System; University of California San Diego
  摘要：Subsampling has seen a resurgence in the big data era where the standard, full-resample size bootstrap can be infeasible to compute. Nevertheless, even choosing a single random subsample of size b can be computationally challenging with both b and the sample size n being very large. This paper shows how a set of appropriately chosen, nonrandom subsamples can be used to conduct effective, and computationally feasible, subsampling distribution estimation. Furthermore, the same set of subsamples ...