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作者：Zhang, Lu; Tang, Wenpin; Banerjee, Sudipto
作者单位：University of Southern California; Columbia University; University of California System; University of California Los Angeles
摘要：We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct spatial predictions at arbitrary locations. We exploit analytically tractable posterior distributions for regression coefficients of predictors and the realizations of the spatial process conditional upon process parameters. We subsequently combine such inference by stacking these models across the range of values of t...
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作者：Llamazares-Elias, L.; Latz, J.; Lindgren, F.
作者单位：University of Edinburgh; Heriot Watt University; University of Edinburgh; University of Manchester; Lancaster University
摘要：Gaussian random fields (GFs) are fundamental tools in spatial modeling and can be represented flexibly and efficiently as solutions to stochastic partial differential equations (SPDEs). The SPDEs depend on specific parameters, which enforce various field behaviors and can be estimated using Bayesian inference. However, even under in-fill asymptotics, the likelihood only provides limited insights into the covariance structure. In response, it is essential to leverage priors to achieve appropria...
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作者：Yang, Jingyuan; Li, Tao; Wang, Tianyi; Ma, Shuangge; Wu, Mengyun
作者单位：Yale University
摘要：Estimation of intracellular gene networks has been a critical component of single-cell transcriptomic data analysis, which can provide crucial insights into the complex interplay between genes, facilitating the discovery of the biological basis of human life at single-cell resolution. Despite notable achievements, existing methodologies often falter in their practicality, primarily due to their narrow focus on simplistic linear relationships and inadequate handling of cellular heterogeneity. T...
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作者：Kim, Ilmun; Schrab, Antonin
作者单位：Korea Advanced Institute of Science & Technology (KAIST); University of Cambridge
摘要：Recent years have witnessed growing concerns about the privacy of sensitive data. In response to these concerns, differential privacy has emerged as a rigorous framework for privacy protection, gaining widespread recognition in both academic and industrial domains. While substantial progress has been made in private data analysis, existing methods often suffer from impracticality or a significant loss of statistical efficiency. This article aims to alleviate these concerns in the context of hy...
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作者：Hahn, P. Richard
作者单位：Arizona State University; Arizona State University-Tempe
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作者：Lee, Seong-ho; Richardson, Brian D.; Ma, Yanyuan; Marder, Karen S.; Garcia, Tanya P.
作者单位：University of Seoul; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Columbia University; Cornell University; Weill Cornell Medicine; NewYork-Presbyterian Hospital
摘要：In Huntington disease research, a current goal is to understand how symptoms change prior to a clinical diagnosis. Statistically, achieving this goal entails modeling symptom severity as a function of the covariate time of diagnosis, which is often heavily right-censored in observational studies. Existing estimators that handle right-censored covariates, such as the complete case estimator and maximum likelihood estimator, vary in their statistical efficiency and robustness to misspecification...
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作者：Braunsteins, Peter; Hautphenne, Sophie; Minuesa, Carmen
作者单位：University of New South Wales Sydney; University of Melbourne
摘要：We derive the first conditionally consistent estimators for a class of parametric Markov population models with logistic growth, which are suitable for modeling endangered populations in restricted habitats with a carrying capacity. We focus on discrete-time parametric population-size-dependent branching processes, for which we propose a new class of weighted least-squares estimators based on a single trajectory of population size counts. We establish the consistency and asymptotic normality o...
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作者：Ma, Haiqiang; Nguyen, Thuan; Jiang, Jiming
作者单位：Jiangxi University of Finance & Economics; Oregon Health & Science University; University of California System; University of California Davis
摘要：The mean squared prediction error (MSPE) is widely used in small area estimation (SAE), as well as in other fields of statistics. Despite its popularity, the MSPE is not always practical in that it treats positive error, or over-prediction, and negative error, or under-prediction, equally. In practice, however, the consequences of these two types of errors are often different. This problem has long been known in statistics; however, a practical solution has not received much attention in SAE, ...
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作者：Shi, Jieru; Wu, Zhenke; Dempsey, Walter
作者单位：University of Michigan System; University of Michigan
摘要：Contextual sensing and delivery of digital interventions to improve health outcomes have gained significant traction in behavioral and psychiatric studies. Micro-randomized trials (MRTs) are a common experimental design for obtaining data-driven evidence on the effectiveness of digital interventions where each individual is repeatedly randomized to receive treatments over numerous time points. Throughout the study, individual characteristics and contextual factors around randomization are coll...
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作者：Su, Buxin; Zhang, Jiayao; Collina, Natalie; Yan, Yuling; Li, Didong; Cho, Kyunghyun; Fan, Jianqing; Roth, Aaron; Su, Weijie
作者单位：University of Pennsylvania; University of Pennsylvania; University of Wisconsin System; University of Wisconsin Madison; University of North Carolina; University of North Carolina Chapel Hill; New York University; Princeton University
摘要：We conducted an experiment during the review process of the 2023 International Conference on Machine Learning (ICML), asking authors with multiple submissions to rank their papers based on perceived quality. In total, we received 1342 rankings, each from a different author, covering 2592 submissions. In this article, we present an empirical analysis of how author-provided rankings could be leveraged to improve peer review processes at machine learning conferences. We focus on the Isotonic Mech...
