Biometrika

最新文章

• 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...
• Super-efficient estimation of future conditional hazards based on time-homogeneous high-quality marker information AI翻译
  作者：Bagkavos, D.; Isakson, A.; Mammen, E.; Nielsen, J. P.; Proust-Lima, C.
  作者单位：University of Ioannina; University of London; Ruprecht Karls University Heidelberg; University of London; Institut National de la Sante et de la Recherche Medicale (Inserm); Universite de Bordeaux
  摘要：We introduce a new concept for forecasting future events based on marker information. The model is developed in the nonparametric counting process setting under the assumptions that the marker is of so-called high quality and with a time-homogeneous conditional distribution. Despite the model having nonparametric parts, it is established herein that it attains a parametric rate of uniform consistency and uniform asymptotic normality. In usual nonparametric scenarios, reaching such a fast conve...
• 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...
• Fast and reliable confidence intervals for a variance component AI翻译
  作者：Zhang, Yiqiao; Ekvall, Karl Oskar; Molstad, Aaron J.
  作者单位：State University System of Florida; University of Florida; University of Minnesota System; University of Minnesota Twin Cities
  摘要：We show that in a variance component model, confidence intervals with asymptotically correct uniform coverage probability can be obtained by inverting certain test statistics based on the score for the restricted likelihood. The results hold in settings where the variance component is near or at the boundary of the parameter set. Simulations indicate that the proposed test statistics are approximately pivotal and lead to confidence intervals with near-nominal coverage even in small samples. We...
• Semiparametric local variable selection under misspecification AI翻译
  作者：Rossell, D.; Seong, A. K.; Saez, I.; Guindani, M.
  作者单位：Pompeu Fabra University; University of California System; University of California Irvine; Icahn School of Medicine at Mount Sinai; University of California System; University of California Los Angeles
  摘要：Local variable selection aims to test for the effect of covariates on an outcome within specific regions. We outline a challenge that arises in the presence of nonlinear effects and model misspecification. Specifically, for common semiparametric methods, even slight model misspecification can result in a high false positive rate, in a manner that is highly sensitive to the chosen basis functions. We propose a method based on orthogonal cut splines that avoids false positive inflation for any c...
• Efficient estimation for functional accelerated failure time models AI翻译
  作者：Liu, Changyu; Su, Wen; Liu, Kin-Yat; Yin, Guosheng; Zhao, Xingqiu
  作者单位：Chinese University of Hong Kong; City University of Hong Kong; University of Hong Kong; Hong Kong Polytechnic University
  摘要：We propose a functional accelerated failure time model to characterize the effects of both functional and scalar covariates on the time to event of interest, and provide regularity conditions to guarantee model identifiability. For efficient estimation of model parameters, we develop a sieve maximum likelihood approach where parametric and nonparametric coefficients are bundled with an unknown baseline hazard function in the likelihood function. Not only do the bundled parameters cause immense...
• 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 ...
• Multivariate Gaussian cumulative distribution functions as the marginal likelihood of their dual Bayesian probit models AI翻译
  作者：Fasano, Augusto; Denti, Francesco
  作者单位：University of Turin; University of Padua
  摘要：The computation of multivariate Gaussian cumulative distribution functions is a key step in many statistical procedures, often representing a crucial computational bottleneck. Over the past few decades, efficient algorithms have been proposed to address this problem, mainly using Monte Carlo solutions. This work highlights a connection between the multivariate Gaussian cumulative distribution function and the marginal likelihood of a tailored dual Bayesian probit model. Consequently, any metho...
• 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...
• Modelling directed networks with reciprocity AI翻译
  作者：Feng, Rui; Leng, Chenlei
  作者单位：University of Warwick
  摘要：Asymmetric relational data are becoming increasingly prevalent in diverse fields, underscoring the need for developing directed network models to address the complex challenges posed by the unique structure of such data. Unlike undirected models, directed models can capture reciprocity, the tendency of nodes to form mutual links. This work addresses a fundamental question: what is the effective sample size for modelling reciprocity? We examine this question by analysing the Bernoulli model wit...