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作者：He, Chenxuan; Wang, Feifei; Zhu, Liping
作者单位：Renmin University of China; Renmin University of China; Renmin University of China
摘要：Exploring the emerging knowledge trends within a particular discipline is of great interest to scientific researchers. It helps researchers to understand the historical development of their disciplines and to guide their future research directions. In this work, we focus on the fast-developing discipline, statistics, to investigate its knowledge trend and emerging topics in statistical research. To this end, we collect publications in top-tier statistical journals and statistical-related confe...
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作者：Kornak, John; Young, Karl; Friedman, Eric; Bakas, Konstantinos
作者单位：University of California System; University of California San Francisco; University of California System; University of California San Francisco; King Abdullah University of Science & Technology
摘要：Bayesian image analysis has been instrumental for over 40 years in addressing challenges such as image noise reduction, de-blurring, feature enhancement, and object detection. Despite its success, modeling spatial dependencies inherent to these problems often results in significant computational challenges. This work introduces the Bayesian Image Analysis in Fourier Space (BIFS) framework, which redefines conventional Bayesian modeling for continuous-valued images by transforming the problem i...
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作者：Pandolfi, Andrea; Papaspiliopoulos, Omiros; Zanella, Giacomo
作者单位：Bocconi University; Bocconi University; Bocconi University
摘要：Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random effects. Such matrices are typically sparse; however, the sparsity pattern resembles a multi partite random graph, which does not lend itself well to default sparse linear algebra techniques. Notably, we show that, for typical GLMMs, the Cholesky factor is dense ev...
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作者：Li, Wenhui; Zhang, Xinyu
作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS
摘要：We propose a model averaging method for high-dimensional regression with highly correlated covariates. We use a factor structure to model the covariate dependence, allowing the covariates to be decomposed into two uncorrelated or weakly correlated latent components: common factors and idiosyncratic components. The number of common factors is allowed to diverge. We average estimators from factor-adjusted candidate models with augmented predictors composed of estimated common factors and idiosyn...
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作者：Lemyre, Felix Camirand; Carroll, Raymond J.; Delaigle, Aurore
作者单位：University of Sherbrooke; Texas A&M University System; Texas A&M University College Station
摘要：We consider nonparametric estimation of the density of the long-term trend of a semicontinuous variable observed repeatedly over time. These variables arise when measuring the intensity of an intermittent phenomenon, such as the intake of an episodically consumed nutrient or the concentration of an intermittent toxic substance: when the phenomenon is absent, the measurement is equal to zero; otherwise, it is positive. Semicontinuous data are usually represented by a two-part model describing t...
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作者：Weylandt, Michael; Michailidis, George
作者单位：City University of New York (CUNY) System; Baruch College (CUNY); University of California System; University of California Los Angeles
摘要：Network data are commonly collected in a variety of applications, representing either directly measured or statistically inferred connections between subjects or features of interest. In an increasing number of domains, these networks are collected over time, such as repeated interactions between users of a social media platform, or across multiple subjects, such as in multi-subject neuroimaging studies. When analyzing multiple large networks, dimensionality reduction techniques are often used...
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作者：Park, Seyoung; Lee, Eun Ryung; Kim, Hyunjin; Zhao, Hongyu
作者单位：Yonsei University; Yonsei University; Sungkyunkwan University (SKKU); Yale University
摘要：In high-dimensional multiple response regression problems, the large dimensionality of the coefficient matrix poses a challenge to parameter estimation. To address this challenge, low-rank matrix estimation methods have been developed to facilitate parameter estimation in the high-dimensional regime, where the number of parameters increases with sample size. Despite these methodological advances, accurately predicting multiple responses with limited target data remains a difficult task. To gai...
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作者：Liu, Zejian; Li, Meng
作者单位：Rice University
摘要：Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal modeling strategy and theoretical properties when using GPs for derivatives. In this article, we study a plug-in strategy by differentiating...
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作者：Bai, Jushan
作者单位：Columbia University
摘要：This article studies the problem of efficient estimation of panel data models in the presence of an increasing number of incidental parameters. We formulate the dynamic panel as a simultaneous equations system, and derive the efficiency bound under the normality assumption. We then show that the Gaussian quasi-maximum likelihood estimator (QMLE) applied to the system achieves the efficiency bound without the normality assumption. Comparison of QMLE with the fixed effects approach is made. Supp...
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作者：Koner, Salil
