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作者：Dombowsky, Alexander; Dunson, David B.
作者单位：Duke University; Duke University
摘要：Bayesian clustering typically relies on mixture models, with each component interpreted as a different cluster. After defining a prior for the component parameters and weights, Markov chain Monte Carlo (MCMC) algorithms are commonly used to produce samples from the posterior distribution of the component labels. The data are then clustered by minimizing the expectation of a clustering loss function that favors similarity to the component labels. Unfortunately, although these approaches are rou...
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作者：Sun, Dayu; Peng, Limin; Qiu, Zhiping; Guo, Ying; Manatunga, Amita
作者单位：Emory University; Fujian Normal University
摘要：Tensors, characterized as multidimensional arrays, are frequently encountered in modern scientific studies. Quantile regression has the unique capacity to explore how a tensor covariate influences different segments of the response distribution. In this work, we propose a partial quantile tensor regression (PQTR) framework, which novelly applies the core principle of the partial least squares technique to achieve effective dimension reduction for quantile regression with a tensor covariate. Th...
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作者：Chang, Jinyuan; Fang, Qin; Qiao, Xinghao; Yao, Qiwei
作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of Sydney; University of Hong Kong; University of London; London School Economics & Political Science
摘要：We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series p is large in relation to the length of time series n. Our first step performs an eigenanalysis of a positive definite matrix, which leads to a one-to-one linear transformation for the original high-dimensional functional time series, and the transformed curve series can be segmented into several groups such that any two subseries from any two different ...
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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：Chakraborty, Saptarshi; Su, Zhihua
作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY; State University System of Florida; University of Florida
摘要：The envelope model aims to increase efficiency in multivariate analysis by using dimension reduction techniques. It has been used in many contexts including linear regression, generalized linear models, matrix/tensor variate regression, reduced rank regression, and quantile regression, and has shown the potential to provide substantial efficiency gains. Virtually all of these advances, however, have been made from a frequentist perspective, and the literature addressing envelope models from a ...
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作者：Huling, Jared D.; Greifer, Noah; Chen, Guanhua
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Harvard University; University of Wisconsin System; University of Wisconsin Madison; University of Minnesota System; University of Minnesota Twin Cities
摘要：Studying causal effects of continuous treatments is important for gaining a deeper understanding of many interventions, policies, or medications, yet researchers are often left with observational studies for doing so. In the observational setting, confounding is a barrier to the estimation of causal effects. Weighting approaches seek to control for confounding by reweighting samples so that confounders are comparable across different treatment values. Yet, for continuous treatments, weighting ...
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作者：Kim, Jae Kwang; Rao, J. N. K.; Wang, Zhonglei
作者单位：Iowa State University; Carleton University; Xiamen University; Xiamen University
摘要：Standard statistical methods without taking proper account of the complexity of a survey design can lead to erroneous inferences when applied to survey data due to unequal selection probabilities, clustering, and other design features. In particular, the Type I error rates of hypotheses tests using standard methods can be much larger than the nominal significance level. Methods incorporating design features in testing hypotheses have been proposed, including Wald tests and quasi-score tests th...
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作者：Nagler, Thomas; Vatter, Thibault
作者单位：University of Munich
摘要：Thanks to their ability to capture complex dependence structures, copulas are frequently used to glue random variables into a joint model with arbitrary marginal distributions. More recently, they have been applied to solve statistical learning problems such as regression or classification. Framing such approaches as solutions of estimating equations, we generalize them in a unified framework. We can then obtain simultaneous, coherent inferences across multiple regression-like problems. We der...
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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...
