539期

最新文章

• Discussion of Confidence Intervals for Nonparametric Empirical Bayes Analysis AI翻译
  作者：Xie, Dongyue; Stephens, Matthew
  作者单位：University of Chicago; University of Chicago
• A History of Data Visualization and Graphic Communication AI翻译
  作者：Park, Hyunwoo
  作者单位：Seoul National University (SNU)
• A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development AI翻译
  作者：Hu, Yingtian; Zeydabadinezhad, Mahmoud; Li, Longchuan; Guo, Ying
  作者单位：Emory University; Emory University
  摘要：Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offer unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging datas...
• Optimal Design of Experiments for Implicit Models AI翻译
  作者：Duarte, Belmiro P. M.; Atkinson, Anthony C.; Granjo, Jose F. O.; Oliveira, Nuno M. C.
  作者单位：Instituto Politecnico de Coimbra (IPC); Universidade de Coimbra; Universidade de Coimbra; University of London; London School Economics & Political Science
  摘要：Explicit models representing the response variables as functions of the control variables are standard in virtually all scientific fields. For these models, there is a vast literature on the optimal design of experiments (ODoE) to provide good estimates of the parameters with the use of minimal resources. Contrarily, the ODoE for implicit models is more complex and has not been systematically addressed. Nevertheless, there are practical examples where the models relating the response variables...
• Gaining Outlier Resistance With Progressive Quantiles: Fast Algorithms and Theoretical Studies AI翻译
  作者：She, Yiyuan; Wang, Zhifeng; Shen, Jiahui
  作者单位：State University System of Florida; Florida State University
  摘要：Outliers widely occur in big-data applications and may severely affect statistical estimation and inference. In this article, a framework of outlier-resistant estimation is introduced to robustify an arbitrarily given loss function. It has a close connection to the method of trimming and includes explicit outlyingness parameters for all samples, which in turn facilitates computation, theory, and parameter tuning. To tackle the issues of nonconvexity and nonsmoothness, we develop scalable algor...
• Regression Analysis of Asynchronous Longitudinal Functional and Scalar Data AI翻译
  作者：Li, Ting; Li, Tengfei; Zhu, Zhongyi; Zhu, Hongtu
  作者单位：Shanghai University of Finance & Economics; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; Fudan University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Many modern large-scale longitudinal neuroimaging studies, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, have collected/are collecting asynchronous scalar and functional variables that are measured at distinct time points. The analyses of temporally asynchronous functional and scalar variables pose major technical challenges to many existing statistical approaches. We propose a class of generalized functional partial-linear varying-coefficient models to appropriately de...
• Measuring Housing Vitality from Multi-Source Big Data and Machine Learning AI翻译
  作者：Zhou, Yang; Xue, Lirong; Shi, Zhengyu; Wu, Libo; Fan, Jianqing
  作者单位：Fudan University; Princeton University; Fudan University; Fudan University
  摘要：Measuring timely high-resolution socioeconomic outcomes is critical for policymaking and evaluation, but hard to reliably obtain. With the help of machine learning and cheaply available data such as social media and nightlight, it is now possible to predict such indices in fine granularity. This article demonstrates an adaptive way to measure the time trend and spatial distribution of housing vitality (number of occupied houses) with the help of multiple easily accessible datasets: energy, nig...
• Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data AI翻译
  作者：Xue, Fei; Zhang, Yanqing; Zhou, Wenzhuo; Fu, Haoda; Qu, Annie
  作者单位：University of Pennsylvania; Yunnan University; University of Illinois System; University of Illinois Urbana-Champaign; Eli Lilly; University of California System; University of California Irvine
  摘要：An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets to maximize the conditional survival function of patients following a DTR. In contrast to most existing approaches which are designed t...
• Rejoinder AI翻译
  作者：Zhou, Yang; Xue, Lirong; Shi, Zhengyu; Wu, Libo; Fan, Jianqing
  作者单位：Fudan University; Fudan University; Fudan University; Princeton University
• Semiparametric Multinomial Logistic Regression for Multivariate Point Pattern Data AI翻译
  作者：Hessellund, Kristian Bjorn; Xu, Ganggang; Guan, Yongtao; Waagepetersen, Rasmus
  作者单位：Aalborg University; University of Miami
  摘要：We propose a new method for analysis of multivariate point pattern data observed in a heterogeneous environment and with complex intensity functions. We suggest semiparametric models for the intensity functions that depend on an unspecified factor common to all types of points. This is for example well suited for analyzing spatial covariate effects on events such as street crime activities that occur in a complex urban environment. A multinomial conditional corn posite likelihood function is i...