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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. ...
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作者：Ding, Fei; He, Shiyuan; Jones, David E.; Meng, Xiao-Li
作者单位：Texas A&M University System; Texas A&M University College Station; Beijing Technology & Business University; Harvard University
摘要：Monte Carlo integration is a powerful tool for scientific and statistical computation, but faces significant challenges when multi-modal distributions are involved, even when the mode locations are known. This work introduces novel Monte Carlo sampling and integration estimation strategies for the multi-modal context by leveraging a generalized version of the stochastic Warp-U transformation (Wang, Jones, and Meng). We propose two flexible classes of Warp-U transformations, one based on a gene...
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作者：Porwal, Anupreet; Rodriguez, Abel
作者单位：Alphabet Inc.; Google Incorporated; University of Washington; University of Washington Seattle
摘要：This article introduces Dirichlet process mixtures of block g priors for model selection and prediction in linear models. These priors are extensions of traditional mixtures of g priors that allow for differential shrinkage for various (data-selected) blocks of parameters while fully accounting for the predictors' correlation structure, providing a bridge between the literatures on model selection and continuous shrinkage priors. We show that Dirichlet process mixtures of block g priors are co...
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作者：Zheng, Xiaotian; Kottas, Athanasios; Sanso, Bruno
作者单位：University System of Georgia; University of Georgia; University of California System; University of California Santa Cruz
摘要：We propose a constructive approach to building temporal point processes that incorporate dependence on their history. The dependence is modeled through the conditional density of the duration, that is, the interval between successive event times, using a mixture of first-order conditional densities for each one of a specific number of lagged durations. Such a formulation for the conditional duration density accommodates high-order dynamics, and it thus enables flexible modeling for point proce...
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作者：Wang, Weihao; Xu, Xiangnan; Zhao, Hongyu; Wang, Tao
作者单位：Shanghai Jiao Tong University; University of Sydney; Yale University; Shanghai Jiao Tong University
摘要：Identifying taxa associated with host phenotypes is crucial for understanding host-microbe interactions and their underlying molecular mechanisms. However, analyzing microbiome data presents unique challenges, as the observed abundances of taxa are high-dimensional, compositional, and subject to both sample-specific and taxon-specific biases. Many existing methods for differential abundance testing struggle to balance false discovery rate control with statistical power. In this article, we pro...
