3期

最新文章

• Moving beyond population variable importance: concept, theory and applications of individual variable importance AI翻译
  作者：Dai, Guorong; Shao, Lingxuan; Chen, Jinbo
  作者单位：Fudan University; University of Pennsylvania
  摘要：In a non-parametric regression setting, we introduce a novel concept of 'individual variable importance', which assesses the relevance of certain covariates to an outcome variable among individuals with specific characteristics. This concept holds practical importance for both risk assessment and association identification. For example, it can represent (i) the usefulness of expensive biomarkers in risk prediction for individuals at a specified baseline risk, or (ii) age-specific associations ...
• Adaptive functional principal components analysis AI翻译
  作者：Wang, Sunny G. W.; Patilea, Valentin; Klutchnikoff, Nicolas
  作者单位：Centre National de la Recherche Scientifique (CNRS); Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Universite de Rennes; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite de Rennes
  摘要：Functional data analysis almost always involves smoothing discrete observations into curves, because they are never observed in continuous time and rarely without error. Although smoothing parameters affect the subsequent inference, data-driven methods for selecting these parameters are not well-developed, frustrated by the difficulty of using all the information shared by curves while being computationally efficient. On the one hand, smoothing individual curves in an isolated, albeit sophisti...
• Dynamic modelling of sparse longitudinal data and functional snippets with stochastic differential equations AI翻译
  作者：Zhou, Yidong; Mueller, Hans-Georg
  作者单位：University of California System; University of California Davis
  摘要：Sparse functional/longitudinal data have attracted widespread interest due to the prevalence of such data in social and life sciences. A prominent scenario where such data are routinely encountered are accelerated longitudinal studies, where subjects are enrolled in the study at a random time and are only tracked for a short amount of time relative to the domain of interest. The statistical analysis of such functional snippets is challenging since information for far-off-diagonal regions of th...
• Adaptive conformal classification with noisy labels AI翻译
  作者：Sesia, Matteo; Wang, Y. X. Rachel; Tong, Xin
  作者单位：University of Southern California; University of Southern California; University of Sydney; University of Hong Kong
  摘要：This article develops a conformal prediction method for classification tasks that can adapt to random label contamination in the calibration sample, often leading to more informative prediction sets with stronger coverage guarantees compared to existing approaches. This is obtained through a precise characterization of the coverage inflation (or deflation) suffered by standard conformal inferences in the presence of label contamination, which is then made actionable through a new calibration a...
• α-separability and adjustable combination of amplitude and phase model for functional data AI翻译
  作者：Wang, Tian; Ding, Jimin
  作者单位：Columbia University; Washington University (WUSTL)
  摘要：We consider separating and joint modelling amplitude and phase variations for functional data in an identifiable manner. To rigorously address this separability issue, we introduce the notion of alpha-separability upon constructing a family of alpha-indexed metrics. We bridge alpha-separability with the uniqueness of Fr & eacute;chet mean, leading to the proposed adjustable combination of amplitude and phase model. The parameter alpha allows user-defined modelling emphasis between vertical and...
• Robust angle-based transfer learning in high dimensions AI翻译
  作者：Gu, Tian; Han, Yi; Duan, Rui
  作者单位：Columbia University; Columbia University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Transfer learning improves target model performance by leveraging data from related source populations, especially when target data are scarce. This study addresses the challenge of training high-dimensional regression models with limited target data in the presence of heterogeneous source populations. We focus on a practical setting where only parameter estimates of pretrained source models are available, rather than individual-level source data. For a single source model, we propose a novel ...
• Prediction sets for high-dimensional mixture of experts models AI翻译
  作者：Javanmard, Adel; Shao, Simeng; Bien, Jacob
  作者单位：University of Southern California; Amazon.com
  摘要：Large datasets make it possible to build predictive models that can capture heterogenous relationships between the response variable and features. The mixture of high-dimensional linear experts model posits that observations come from a mixture of high-dimensional linear regression models, where the mixture weights are themselves feature-dependent. In this article, we show how to construct valid prediction sets for an & ell;1-penalized mixture of experts model in the high-dimensional setting. ...
• Causal mediation analysis: selection with asymptotically valid inference AI翻译
  作者：Jones, Jeremiah; Ertefaie, Ashkan; Strawderman, Robert L.
  作者单位：University of Rochester; Eli Lilly
  摘要：Researchers are often interested in learning not only the effect of treatments on outcomes, but also the mechanisms that transmit these effects. A mediator is a variable that is affected by treatment and subsequently affects outcome. Existing methods for penalized mediation analyses may lead to ignoring important mediators and either assume that finite-dimensional linear models are sufficient to remove confounding bias, or perform no confounding control at all. In practice, these assumptions m...
• Scalable couplings for the random walk Metropolis algorithm AI翻译
  作者：Papp, Tamas P.; Sherlock, Chris
  作者单位：Lancaster University; Lancaster University
  摘要：There has been a recent surge of interest in coupling methods for Markov chain Monte Carlo algorithms: they facilitate convergence quantification and unbiased estimation, while exploiting embarrassingly parallel computing capabilities. Motivated by these, we consider the design and analysis of couplings of the random walk Metropolis algorithm which scale well with the dimension of the target measure. Methodologically, we introduce a low-rank modification of the synchronous coupling that is pro...
• Frequentist inference for semi-mechanistic epidemic models with interventions AI翻译
  作者：Bong, Heejong; Ventura, Valerie; Wasserman, Larry
  作者单位：University of Michigan System; University of Michigan; Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
  摘要：The effect of public health interventions on an epidemic are often estimated by adding the intervention to epidemic models. During the Covid-19 epidemic, numerous papers used such methods for making scenario predictions. The majority of these papers use Bayesian methods to estimate the parameters of the model. In this article, we show how to use frequentist methods for estimating these effects which avoids having to specify prior distributions. We also use model-free shrinkage methods to impro...