3期

最新文章

• A spike-and-slab prior for dimension selection in generalized linear network eigenmodels AI翻译
  作者：Loyal, Joshua D.; Chen, Yuguo
  作者单位：State University System of Florida; Florida State University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Latent space models are often used to model network data by embedding a network's nodes into a low-dimensional latent space; however, choosing the dimension of this space remains a challenge. To this end, we begin by formalizing a class of latent space models we call generalized linear network eigenmodels that can model various edge types (binary, ordinal, nonnegative continuous) found in scientific applications. This model class subsumes the traditional eigenmodel by embedding it in a general...
• A general form of covariate adjustment in clinical trials under covariate-adaptive randomization AI翻译
  作者：Bannick, Marlena S.; Shao, Jun; Liu, Jingyi; Du, Yu; Yi, Yanyao; Ye, Ting
  作者单位：University of Washington; University of Washington Seattle; University of Wisconsin System; University of Wisconsin Madison; Eli Lilly
  摘要：In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted estimator, which is a general form of covariate adjustment that uses linear, generalized linear and nonparametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we establish general theorems that sho...
• Dynamic factor analysis of high-dimensional recurrent events AI翻译
  作者：Chen, F.; Chen, Y.; Ying, Z.; Zhou, K.
  作者单位：Columbia University; University of London; London School Economics & Political Science; Columbia University
  摘要：Recurrent event time data arise in many studies, including in biomedicine, public health, marketing and social media analysis. High-dimensional recurrent event data involving many event types and observations have become prevalent with advances in information technology. This article proposes a semiparametric dynamic factor model for the dimension reduction of high-dimensional recurrent event data. The proposed model imposes a low-dimensional structure on the mean intensity functions of the ev...
• Bias correction of quadratic spectral estimators AI翻译
  作者：Astfalck, Lachlan C.; Sykulski, Adam M.; Cripps, Edward J.
  作者单位：University of Western Australia; Imperial College London
  摘要：The three cardinal, statistically consistent families of nonparametric estimators for the power spectral density of a time series are the lag-window, multitaper and Welch estimators. However, when estimating power spectral densities from a finite sample, each can be subject to nonignorable bias. Astfalck et al. (2024) developed a method that offers significant bias reduction for finite samples for Welch's estimator, which this article extends to the larger family of quadratic estimators, thus ...
• Consistent and scalable composite likelihood estimation of probit models with crossed random effects AI翻译
  作者：Bellio, R.; Ghosh, S.; Owen, A. B.; Varin, C.
  作者单位：University of Udine; Stanford University; Universita Ca Foscari Venezia
  摘要：Estimation of crossed random effects models commonly incurs computational costs that grow faster than linearly in the sample size $ N $, often as fast as $ \Omega(N<^>{3/2}) $, making them unsuitable for large datasets. For non-Gaussian responses, integrating out the random effects to obtain a marginal likelihood poses significant challenges, especially for high-dimensional integrals for which the Laplace approximation may not be accurate. In this article we develop a composite likelihood appr...
• Integer programming for learning directed acyclic graphs from nonidentifiable Gaussian models AI翻译
  作者：Xu, Tong; Taeb, Armeen; Kucukyavuz, Simge; Shojaie, Ali
  作者单位：Northwestern University; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: (i) they cannot provide optimality guarantees and can suffer from learning suboptimal models; (ii) they rely on the stringent assumption that the noise is homoscedastic, and hence the underlying model is fully identifiable. We ...
• Transfer learning for piecewise-constant mean estimation: optimality, l1 and l0 penalization AI翻译
  作者：Wang, F.; Yu, Y.
  作者单位：University of Warwick
  摘要：We study transfer learning for estimating piecewise-constant signals when source data, which may be relevant but disparate, are available in addition to target data. We first investigate transfer learning estimators that respectively employ l(0) and l(1) penalties for unisource data scenarios and then generalize these estimators to accommodate multisources. To further reduce estimation errors, especially when some sources significantly differ from the target, we introduce an informative source...
• Predictive performance of power posteriors AI翻译
  作者：McLatchie, Y.; Fong, E.; Frazier, D. T.; Knoblauch, J.
  作者单位：University of London; University College London; University of Hong Kong; Monash University
  摘要：We analyse the impact of using tempered likelihoods in the production of posterior predictions. While the choice of temperature has an impact on predictive performance in small samples, we formally show that in moderate-to-large samples, tempering does not impact posterior predictions.
• A general condition for bias attenuation by a nondifferentially mismeasured confounder AI翻译
  作者：Zhang, Jeffrey; Lee, Junu
  作者单位：University of Pennsylvania
  摘要：In real-world studies, the collected confounders may suffer from measurement error. Although mismeasurement of confounders is typically unintentional (originating from sources such as human oversight or imprecise machinery), deliberate mismeasurement also occurs and is becoming increasingly more common. For example, in the 2020 U.S. census, noise was added to measurements to assuage privacy concerns. Sensitive variables such as income or age are often partially censored and are only known up t...
• Identifying and bounding the probability of necessity for causes of effects with ordinal outcomes AI翻译
  作者：Zhang, Chao; Geng, Zhi; Li, Wei; Ding, Peng
  作者单位：Beijing Technology & Business University; Renmin University of China; Renmin University of China; University of California System; University of California Berkeley
  摘要：Although the existing causal inference literature focuses on the forward-looking perspective by estimating effects of causes, the backward-looking perspective can provide insights into causes of effects. In backward-looking causal inference, the probability of necessity measures the probability that a certain event is caused by the treatment, given the observed treatment and outcome. Most existing results focus on binary outcomes. Motivated by applications with ordinal outcomes, we propose a g...