2期

最新文章

• HIERARCHICAL PROBABILISTIC CONFORMAL PREDICTION FOR DISTRIBUTED ENERGY RESOURCES ADOPTION AI翻译
  作者：Zhou, Wenbin; Zhu, Shixiang
  作者单位：Carnegie Mellon University
  摘要：The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management. Accurately predicting DER adoption is critical for proactive infrastructure planning, but the inherent uncertainty and spatial disparity of DER growth complicate traditional forecasting approaches. Moreover, the hierarchical structure of distribution grids demands that predictions satisfy statistical guarantees at both the circuit and substation levels, a...
• AUTOREGRESSIVE MODELS FOR PANEL DATA CAUSAL INFERENCE WITH APPLICATION TO STATE-LEVEL OPIOID POLICIES AI翻译
  作者：Antonelli, Joseph; Rubinstein, Max; Agniel, Denis; Smart, Rosanna; Stuart, Elizabeth A.; Cefalu, Matthew; Schell, Terry; Eagan, Joshua; Stone, Elizabeth; Griswold, Max; Griffin, Beth Ann
  作者单位：State University System of Florida; University of Florida; RAND Corporation; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：Motivated by the study of state opioid policies, we propose a novel approach using autoregressive models for causal effect estimation in panel data settings. We estimate the impact of key opioid-related policies, specifically must-access prescription drug monitoring programs (PDMPs), naloxone access laws (NALs), and medical marijuana laws, on opioid prescribing. Existing methods, such as difference-in-differences and synthetic controls, are difficult to apply in dynamic policy environments wit...
• A TIME WARPING MODEL FOR SEASONAL DATA WITH APPLICATION TO AGE ESTIMATION FROM NARWHAL TUSKS AI翻译
  作者：Reiter, Lars N.; Hoffmann, Adam G.; Heide-Jorgensen, Mads Peter; Garde, Eva; Samson, Adeline; Ditlevsen, Susanne
  作者单位：University of Copenhagen; Greenland Institute of Natural Resources; Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA)
  摘要：Signals with varying periodicity frequently appear in real-world phenomena, necessitating the development of efficient modelling techniques to map the measured nonlinear timeline to linear time. Here we propose a regression model that allows for a representation of periodic and dynamic patterns observed in time series data. The model incorporates a hidden strictly positive stochastic process that represents the instantaneous frequency, allowing the model to adapt and accurately capture varying...
• EXPLAINABLE PARAMETER CALIBRATION VIA IMPORTANCE-DRIVEN SEQUENTIAL DESIGN WITH AN APPLICATION TO BUILDING ENERGY SYSTEMS AI翻译
  作者：Jeong, Cheoljoon; Byon, Eunshin
  作者单位：Clemson University; University of Michigan System; University of Michigan
  摘要：Parameter calibration seeks to estimate unobservable parameters in a computer model by aligning field observations with computer model outputs. In the building energy sector, a physics-based computer model is developed to analyze building energy use, given various weather conditions and operational scenarios. To obtain accurate simulations, it is necessary to calibrate model parameters required for preconfiguration. Among various techniques, Bayesian optimization stands out for its potential b...
• LÉVY PROCESSES FOR JUMPING GROWTH OF SPINY LOBSTERS, Panuuirus ornatus AI翻译
  作者：Wang, You-gan; Foo, Chuan hui
  作者单位：Guangdong University of Finance & Economics; Universiti Pendidikan Sultan Idris
  摘要：The discontinuous moulting process in crustaceans poses fundamental challenges for growth modelling and can lead to biologically implausible estimates of asymptotic size under traditional continuous-growth frameworks such as the von Bertalanffy curve. We develop a stochastic growth model that jointly characterises moult increment (MI) and intermoult period (IP) through a unified convolution-based likelihood. Individual growth is represented within a L & eacute;vy-inspired jump framework that e...
• EVALUATING A MULTIPLEX DIAGNOSTIC TEST USING PARTIALLY ORDERED BAYES CLASSIFIER AI翻译
  作者：Cheung, Ying Kuen; Kuhn, Louise
  作者单位：Columbia University; Columbia University
  摘要：In a diagnostic test using multiplex assay, each individual biomarker is often expected to have monotonic association with the disease outcome, and, therefore, the underlying disease classification rule is partially ordered with respect to the biomarkers. Nonparametric estimation of the classification rule can be accomplished by projecting an unconstrained Bayes estimator onto the partial ordering subspace. However, computing the projection is challenging as it involves performing maximization...
• GENERATIVE SCORE INFERENCE FOR MULTIMODAL DATA AI翻译
  作者：Tian, Xinyu; Shen, Xiaotong
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：Accurate uncertainty quantification is crucial for making reliable decisions in various supervised learning scenarios, particularly when dealing with complex, multimodal data such as images and text. Current approaches often face notable limitations, including rigid assumptions and limited generalizability, constraining their effectiveness across diverse supervised learning tasks. To overcome these limitations, we introduce Generative Score Inference (GSI), a flexible inference framework capab...
• PERTURBATION-ROBUST PREDICTIVE MODELING OF SOCIAL EFFECTS BY NETWORK SUBSPACE GENERALIZED LINEAR MODELS AI翻译
  作者：Wang, Jianxiang; Le, An M.; Li, Tianxi
  作者单位：Rutgers University System; University of California System; University of California Davis; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Network-linked data, in which multivariate observations are interconnected by a network, are becoming increasingly prevalent in fields such as sociology and biology. These data often exhibit inherent noise and complex relational structures, complicating conventional modeling and statistical inference. Motivated by empirical challenges in analyzing such datasets, this paper introduces a family of network subspace generalized linear models designed for analyzing noisy, network-linked data. We pr...
• GRANGER CAUSALITY FOR MIXED TIME SERIES GENERALIZED LINEAR MODELS: A CASE STUDY ON MULTIMODAL BRAIN CONNECTIVITY AI翻译
  作者：Piancastelli, Luiza S. C.; Barreto-Souza, Wagner; Fortin, Norbert J.; Cooper, Keiland W.; Ombao, Hernando
  作者单位：University College Dublin; University of California System; University of California Irvine; University of California System; University of California Irvine; King Abdullah University of Science & Technology; King Abdullah University of Science & Technology
  摘要：This paper is motivated by neuroscience studies aimed at understanding causal or predictive interactions between nodes in a brain network using multimodal brain activity data. To assess Granger causality, we introduce a flexible framework through a general class of models that accommodate mixed types of data (binary, count, continuous and positive components) formulated in a generalized linear model (GLM) fashion. To conduct statistical inference for causality, we propose a Bayesian mixed time...
• INFERENCE ON SUMMARIES OF A MODEL-AGNOSTIC LONGITUDINAL VARIABLE IMPORTANCE TRAJECTORY WITH APPLICATION TO SUICIDE PREVENTION AI翻译
  作者：Williamson, Brian D.; Moodie, Erica E. M.; Simon, Gregory E.; Rossom, Rebecca C.; Shortreed, Susan M.
  作者单位：Kaiser Permanente; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle; McGill University; Kaiser Permanente; Kaiser Permanente; University of Washington; University of Washington Seattle; HealthPartners Institute for Education & Research
  摘要：Risk of suicide attempt varies over time. Understanding the importance of risk factors measured at a mental health visit can help clinicians evaluate future risk and provide appropriate care during the visit. In prediction settings where data are collected over time, such as in mental health care, it is often of interest to understand both the importance of variables for predicting the response at each time point and the importance summarized over the time series. Building on recent advances i...