4期

最新文章

• A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS AI翻译
  作者：Zou, Jingjing; Lin, Tuo; Di, Chongzhi; Bellettiere, John; Jankowska, Marta m.; Hartman, Sheri j.; Sears, Dorothy d.; Lacroix, Andrea z.; Rock, Cheryl l.; Natarajan, Loki
  作者单位：University of California System; University of California San Diego; Fred Hutchinson Cancer Center; City of Hope; Beckman Research Institute of City of Hope; Arizona State University; Arizona State University-Tempe; University of California System; University of California San Diego
  摘要：Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics in which important information of PA temporal/diurnal patterns is lost. In this paper we pro...
• DATA-DRIVEN CHIMNEY FIRE RISK PREDICTION USING MACHINE LEARNING AND POINT PROCESS TOOLS AI翻译
  作者：Lu, Changqin; Van Lieshout, Marie-colette; De Graaf, Maurits; Visscher, Paul
  作者单位：University of Twente; Thales Group
  摘要：Chimney fires constitute one of the most commonly occurring fire types. Precise prediction and prompt prevention are crucial in reducing the harm they cause. In this paper we develop a combined machine learning and statistical modelling process to predict fire risk. First, we use random forests and permutation importance techniques to identify the most informative explanatory variables. Second, we design a Poisson point process model and employ logistic regression estimation to estimate the pa...
• WHEN ECOLOGICAL INDIVIDUAL HETEROGENEITY MODELS AND LARGE DATA COLLIDE: AN IMPORTANCE SAMPLING APPROACH AI翻译
  作者：King, Ruth; Sarzo, Blanca; Elvira, Victor
  作者单位：University of Edinburgh; Heriot Watt University; University of Edinburgh; University of Valencia
  摘要：We consider the challenges that arise when fitting ecological individual heterogeneity models to large data sets. In particular, we focus on dividual heterogeneity present in ecological populations within the context of capture-recapture data, although the approach is more widely applicable to more general latent variable models. Within such models the associated likelihood is expressible only as an analytically intractable integral. Common techniques for fitting such models to data include, f...
• STOCHASTIC DECLUSTERING OF EARTHQUAKES WITH THE SPATIOTEMPORAL RENEWAL ETAS MODEL AI翻译
  作者：Stindl, Tom; Chen, Feng
  摘要：Modeling and forecasting earthquakes is challenging due to the complex interplay and clustering of main-shocks and aftershocks. The epidemic-type aftershock sequence (ETAS) model represents the conditional intensity of earthquakes as the superposition of a background and aftershock rate which allows for the declustering of the earthquakes. Its success has led to the development of numerous versions of the ETAS model. Among these extensions is the renewal ETAS (RETAS) model, which has shown pro...
• A RELUCTANT ADDITIVE MODEL FRAMEWORK FOR INTERPRETABLE NONLINEAR INDIVIDUALIZED TREATMENT RULES AI翻译
  作者：Maronge, Jacob M.; Huling, Jared D.; Chen, Guanhua
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison
  摘要：Individualized treatment rules (ITRs) for treatment recommendation is an important topic for precision medicine as not all beneficial treatments work well for all individuals. Interpretability is a desirable property of ITRs, as it helps practitioners make sense of treatment decisions, yet there is a need for ITRs to be flexible to effectively model complex biomedical data for treat-ment decision making. Many ITR approaches either focus on linear ITRs, which may perform poorly when true optima...
• ASSOCIATION AND CAUSATION: ATTRIBUTES AND EFFECTS OF JUDGES IN EQUAL EMPLOYMENT OPPORTUNITY COMMISSION LITIGATION OUTCOMES AI翻译
  作者：Sobel, Michael E.; Wawro, Gregory J.; Farhang, Sean
  作者单位：Columbia University; Columbia University; University of California System; University of California Berkeley
  摘要：A large literature on judicial decision making asks if judges with different features of an attribute (e.g, sex, race) adjudicate cases differently. Researchers estimate models for case outcomes, interpreting coefficients associated with attributes as effects. But attributes are not treatments. While these coefficients indicate how judges with different features adjudicate the different cases they are assigned, ideally, different judges should be compared on a common set of cases. We construct...
• OPTIMAL SAMPLING DESIGNS FOR MULTIDIMENSIONAL STREAMING TIME SERIES WITH APPLICATION TO POWER GRID SENSOR DATA AI翻译
  作者：Xie, Rui; Bai, Shuyang; Ma, Ping
  作者单位：State University System of Florida; University of Central Florida; University System of Georgia; University of Georgia
  摘要：The Internet of Things (IoT) system generates massive high-speed temporally correlated streaming data and is often connected with online inference tasks under computational or energy constraints. Online analysis of these streaming time series data often faces a trade-off between statistical efficiency and computational cost. One important approach to balance this trade-off is sampling, where only a small portion of the sample is selected for the model fitting and update. Motivated by the deman...
• A STATISTICAL APPROACH TO ESTIMATING ADSORPTION-ISOTHERM PARAMETERS IN GRADIENT-ELUTION PREPARATIVE LIQUID CHROMATOGRAPHY AI翻译
  作者：Su, Jiaji; Yao, Zhigang; Li, Cheng; Zhang, Ye
  作者单位：National University of Singapore; Shenzhen MSU-BIT University
  摘要：Determining the adsorption isotherms is an issue of significant importance in preparative chromatography. A modern technique for estimating adsorption isotherms is to solve an inverse problem so that the simulated batch separation coincides with actual experimental results. However, due to the ill-posedness, the high nonlinearity, and the uncertainty quantification of the corresponding physical model, the existing deterministic inversion methods are usually inefficient in real-world applicatio...
• MODEL-INDEPENDENT DETECTION OF NEW PHYSICS SIGNALS USING INTERPRETABLE SEMISUPERVISED CLASSIFIER TESTS AI翻译
  作者：Chakravarti, Purvasha; Kuusela, Mikael; Lei, Jing; Wasserman, Larry
  作者单位：University of London; University College London; Carnegie Mellon University; Carnegie Mellon University
  摘要：A central goal in experimental high energy physics is to detect new physics signals that are not explained by known physics. In this paper we aim to search for new signals that appear as deviations from known Standard Model physics in high-dimensional particle physics data. To do this, we determine whether there is any statistically significant difference between the distribution of Standard Model background samples and the distribution of the experimental observations which are a mixture of t...