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作者：Wang, Chi-Hua; Wang, Zhanyu; Sun, Will Wei; Cheng, Guang
作者单位：University of California System; University of California Los Angeles; Purdue University System; Purdue University; Purdue University System; Purdue University
摘要：Devising a dynamic pricing policy with always valid online statistical learning procedures is an important and as yet unresolved problem. Most existing dynamic pricing policies, which focus on the faithfulness of adopted customer choice models, exhibit a limited capability for adapting to the online uncertainty of learned statistical models during the pricing process. In this article, we propose a novel approach for designing a dynamic pricing policy based on regularized online statistical lea...
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作者：Chakraborty, Antik; Ou, Rihui; Dunson, David B.
作者单位：Purdue University System; Purdue University; Duke University
摘要：It has become increasingly common to collect high-dimensional binary response data; for example, with the emergence of new sampling techniques in ecology. In smaller dimensions, multivariate probit (MVP) models are routinely used for inferences. However, algorithms for fitting such models face issues in scaling up to high dimensions due to the intractability of the likelihood, involving an integral over a multivariate normal distribution having no analytic form. Although a variety of algorithm...
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作者：Ensor, Katherine B.
作者单位：Rice University
摘要：Statistical foundations are without question at the core of modern innovation. In today's economy, a common phrase is data is the new gold. Certainly, we live in an age where data is large, ubiquitous, and comes in many forms. The contributions from the statistical sciences go beyond data. We are emerging from a pandemic where statisticians around the globe saved lives by contributing critical understanding to vaccines, treatments, pandemic policies, and management. The contributions from stat...
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作者：Chen, Ziyuan; Yang, Ying; Yao, Fang
作者单位：Peking University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS
摘要：Matrix recovery from sparse observations is an extensively studied topic emerging in various applications, such as recommendation system and signal processing, which includes the matrix completion and compressed sensing models as special cases. In this article, we propose a general framework for dynamic matrix recovery of low-rank matrices that evolve smoothly over time. We start from the setting that the observations are independent across time, then extend to the setting that both the design...
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作者：Cao, Yang; Sun, Xinwei; Yao, Yuan
作者单位：Hong Kong University of Science & Technology; Fudan University
摘要：Multiple comparisons in hypothesis testing often encounter structural constraints in various applications. For instance, in structural Magnetic Resonance Imaging for Alzheimer's Disease, the focus extends beyond examining atrophic brain regions to include comparisons of anatomically adjacent regions. These constraints can be modeled as linear transformations of parameters, where the sign patterns play a crucial role in estimating directional effects. This class of problems, encompassing total ...
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作者：Cattaneo, Matias D.; Feng, Yingjie; Underwood, William G.
作者单位：Princeton University; Tsinghua University
摘要：Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many other data science disciplines. We consider the problem of uniformly estimating a dyadic Lebesgue density function, focusing on nonparametric kernel-based estimators taking the form of dyadic empirical processes. Our main contributions include the minimax-optimal uniform convergence rate of the dyadic kernel density est...
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作者：Liu, Mochuan; Wang, Yuanjia; Fu, Haoda; Zeng, Donglin
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Columbia University; Eli Lilly; University of Michigan System; University of Michigan
摘要：Dynamic treatment regimen (DTR) is one of the most important tools to tailor treatment in personalized medicine. For many diseases such as cancer and type 2 diabetes mellitus (T2D), more aggressive treatments can lead to a higher efficacy but may also increase risk. However, few methods for estimating DTRs can take into account both cumulative benefit and risk. In this work, we propose a general statistical learning framework to learn optimal DTRs that maximize the reward outcome while control...
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作者：Zhong, Wei; Li, Zhuoxi; Guo, Wenwen; Cui, Hengjian
作者单位：Xiamen University; Xiamen University; Capital Normal University
摘要：We propose a new measure of dependence between a categorical random variable and a random vector with potentially high dimensions, named semi-distance correlation. It is an interesting extension of distance correlation to accommodate the information of the categorical random variable. It equals zero if and only if the categorical random variable and the other random vector are independent. Two important applications of semi-distance correlation are considered. First, we develop a semi-distance...
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作者：Zimmerman, Robert; Craiu, Radu V.; Leos-Barajas, Vianey
作者单位：University of Toronto; University of Toronto
摘要：We propose a copula-based extension of the hidden Markov model (HMM) which applies when the observations recorded at each time in the sample are multivariate. The joint model produced by the copula extension allows decoding of the hidden states based on information from multiple observations. However, unlike the case of independent marginals, the copula dependence structure embedded into the likelihood poses additional computational challenges. We tackle the latter using a theoretically-justif...
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作者：Vogels, Lucas; Mohammadi, Reza; Schoonhoven, Marit; Birbil, S. Ilker
作者单位：University of Amsterdam
摘要：Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is known as structure learning. Bayesian methods can measure the uncertainty of conditional relationships and include prior information. However, frequentist methods are often preferred due to the computational burden of the Bayesian approach. Over the last decade, Bayesian methods have seen substantia...
