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作者：Namdari, Jamshid; Manatunga, Amita; Ferrarelli, Fabio; Krafty, Robert T.
作者单位：Emory University; Rollins School Public Health; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：Principal component analysis has been a main tool in multivariate analysis for estimating a low dimensional linear subspace that explains most of the variability in the data. However, in high-dimensional regimes, naive estimates of the principal loadings are not consistent and difficult to interpret. In the context of time series, principal component analysis of spectral density matrices can provide valuable, parsimonious information about the behavior of the underlying process, particularly i...
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作者：Zhang, Likun; Ma, Xiaoyu; Wikle, Christopher K.; Huser, Raphael
作者单位：University of Missouri System; University of Missouri Columbia; King Abdullah University of Science & Technology
摘要：Many real-world processes have complex tail dependence structures that cannot be adequately characterized using classical Gaussian processes. Alternatively, models motivated by extreme-value theory exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from in high dimensions using classical methods. In this article, we extend the boundaries on computation and modeling of high-dimensional spatial extremes by integrating a new flexible and non...
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作者：Wang, Zeya; Ye, Chenglong
作者单位：University of Kentucky
摘要：Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensional embeddings before partitioning, which embarks unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering for two reasons: (a) the curse of dimensionality when applied to the high-dimensional input data, and (b) unreliable comparison of clustering results when...
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作者：He, Di; Zou, Hui
作者单位：Nanjing University; University of Minnesota System; University of Minnesota Twin Cities
摘要：Canonical correlation analysis (CCA) is an important statistical technique that explores the linear relationships between two sets of variables. In this article, we propose a new generalization of CCA named sparse Gaussianized CCA (SGCCA) for high-dimensional data analysis. SGCCA has a number of favorable properties. First, it is conceptually easy to comprehend and efficient to implement. Second, it not only yields sparse and nested canonical vectors, but is also invariant against monotone tra...
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作者：Mao, Lu
作者单位：University of Wisconsin System; University of Wisconsin Madison
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作者：Linero, Antonio R.
作者单位：University of Texas System; University of Texas Austin
摘要：A recent trend in Bayesian research has been revisiting generalizations of the likelihood that enable Bayesian inference without requiring the specification of a data generating mechanism. This article focuses on a Bayesian nonparametric extension of Wedderburn's quasi-likelihood, using Bayesian additive regression trees to model the mean function. Here, the analyst posits only a structural relationship between the mean and variance of the outcome. We show that this approach provides a unified...
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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：Wang, Zhijing; Xu, Peirong; Zhao, Hongyu; Wang, Tao
作者单位：Shanghai Jiao Tong University; Yale University; Shanghai Jiao Tong University; Shanghai Jiao Tong University
摘要：The Poisson factor model is a powerful tool for dimension reduction and visualization of large-scale count datasets across diverse domains. Despite the availability of efficient algorithms for estimating factors and loadings, existing methods either require prior knowledge of the number of factors, or resort to ad hoc criteria for its determination. This article proposes a novel data-driven criterion called Information Criterion via Data Thinning (ICDT), leveraging the thinning property of the...
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作者：Meng, Xuran; Cao, Yuan; Wang, Weichen
作者单位：University of Michigan System; University of Michigan; University of Hong Kong
摘要：Portfolio optimization aims at constructing a realistic portfolio with significant out-of-sample performance, which is typically measured by the out-of-sample Sharpe ratio. However, due to in-sample optimism, it is inappropriate to use the in-sample estimated covariance to evaluate the out-of-sample Sharpe, especially in the high dimensional settings. In this article, we propose a novel method to estimate the out-of-sample Sharpe ratio using only in-sample data, based on random matrix theory. ...
