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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 ...
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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...
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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...
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作者：Rudolph, K. E.; Williams, N. T.; Stuart, E. A.; Diaz, I
作者单位：Columbia University; Johns Hopkins University; New York University
摘要：We develop flexible, semiparametric estimators of the average treatment effect transported to a new target population, which offer potential efficiency gains. Transport may be of value when the average treatment effect may differ across populations. We consider the setting where differences in the average treatment effect are due to differences in the distribution of effect modifiers, baseline covariates that modify the treatment effect. First, we propose a collaborative one-step semiparametri...
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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 ...
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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...
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作者：Li, Tianxi; Levina, Elizaveta; Zhu, Ji
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作者：Martinussen, Torben; Vansteelandt, Stijn
作者单位：University of Copenhagen; Ghent University
摘要：Cox regression is the default approach to evaluating the (relative) effect of two treatments on a survival endpoint. This standard framework has nonetheless been criticized for its canonical effect measure, the hazard ratio, having a subtle interpretation, thereby hindering policy-making. This in turn has prompted interest in other effects measures, such as the difference in restricted mean survival time, the net benefit and the win ratio, which have become increasingly popular. Developments i...
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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.
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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...
