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作者：Song, Xiaoyu; Ji, Jiayi; Wang, Pei
作者单位：Icahn School of Medicine at Mount Sinai; Icahn School of Medicine at Mount Sinai; Icahn School of Medicine at Mount Sinai; Icahn School of Medicine at Mount Sinai
摘要：Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over six million deaths in the ongoing COVID-19 pandemic. SARS-CoV-2 uses ACE2 protein to enter human cells, raising a pressing need to characterize proteins/pathways interacted with ACE2. Large-scale proteomic profiling technology is not mature at single-cell resolution to examine the protein activities in disease-relevant cell types. We propose iProMix, a novel statistical framework to identify epithelial-cell specific a...
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作者：Cai, Zhibo; Xia, Yingcun; Hang, Weiqiang
作者单位：National University of Singapore; University of Electronic Science & Technology of China
摘要：Sufficient dimension reduction (SDR) has progressed steadily. However, its ability to improve general function estimation or classification has not been well received, especially for high-dimensional data. In this article, we first devise a local linear smoother for high dimensional nonparametric regression and then utilise it in the outer-product-of-gradient (OPG) approach of SDR. We call the method high-dimensional OPG (HOPG). To apply SDR to classification in high-dimensional data, we propo...
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作者：Cai, T. Tony; Guo, Zijian; Ma, Rong
作者单位：University of Pennsylvania; Rutgers University System; Rutgers University New Brunswick; Stanford University
摘要：This article develops a unified statistical inference framework for high-dimensional binary generalized linear models (GLMs) with general link functions. Both unknown and known design distribution settings are considered. A two-step weighted bias-correction method is proposed for constructing confidence intervals (CIs) and simultaneous hypothesis tests for individual components of the regression vector. Minimax lower bound for the expected length is established and the proposed CIs are shown t...
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作者：Zito, Alessandro; Rigon, Tommaso; Ovaskainen, Otso; Dunson, David B.
作者单位：Duke University; University of Milano-Bicocca; University of Jyvaskyla; University of Helsinki; Norwegian University of Science & Technology (NTNU)
摘要：We aim at modeling the appearance of distinct tags in a sequence of labeled objects. Common examples of this type of data include words in a corpus or distinct species in a sample. These sequential discoveries are often summarized via accumulation curves, which count the number of distinct entities observed in an increasingly large set of objects. We propose a novel Bayesian method for species sampling modeling by directly specifying the probability of a new discovery, therefore, allowing for ...
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作者：Dai, Xiaowu; Li, Lexin
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; University of California System; University of California Berkeley
摘要：Multimodal imaging has transformed neuroscience research. While it presents unprecedented opportunities, it also imposes serious challenges. Particularly, it is difficult to combine the merits of the interpretability attributed to a simple association model with the flexibility achieved by a highly adaptive nonlinear model. In this article, we propose an orthogonalized kernel debiased machine learning approach, which is built upon the Neyman orthogonality and a form of decomposition orthogonal...
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作者：Deng, Yujia; Tang, Xiwei; Qu, Annie
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Virginia; University of California System; University of California Irvine
摘要：Multi-dimensional tensor data have gained increasing attention in the recent years, especially in biomedical imaging analyses. However, the most existing tensor models are only based on the mean information of imaging pixels. Motivated by multimodal optical imaging data in a breast cancer study, we develop a new tensor learning approach to use pixel-wise correlation information, which is represented through the higher order correlation tensor. We proposed a novel semi-symmetric correlation ten...
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作者：Henzi, Alexander; Kleger, Gian-Reto; Ziegel, Johanna F.
作者单位：University of Bern; Kantonsspital St. Gallen
摘要：A Distributional (Single) Index Model (DIM) is a semiparametric model for distributional regression, that is, estimation of conditional distributions given covariates. The method is a combination of classical single-index models for the estimation of the conditional mean of a response given covariates, and isotonic distributional regression. The model for the index is parametric, whereas the conditional distributions are estimated nonparametrically under a stochastic ordering constraint. We sh...
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作者：Nie, Lizhen; Rockova, Veronika
作者单位：University of Chicago; University of Chicago
摘要：The impracticality of posterior sampling has prevented the widespread adoption of spike-and-slab priors in high-dimensional applications. To alleviate the computational burden, optimization strategies have been proposed that quickly find local posterior modes. Trading off uncertainty quantification for computational speed, these strategies have enabled spike-and-slab deployments at scales that would be previously unfeasible. We build on one recent development in this strand of work: the Spike-...
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作者：Yang, Ying; Yao, Fang
作者单位：Peking University
摘要：Functional data analysis has attracted considerable interest and is facing new challenges, one of which is the increasingly available data in a streaming manner. In this article we develop an online nonparametric method to dynamically update the estimates of mean and covariance functions for functional data. The kernel-type estimates can be decomposed into two sufficient statistics depending on the data-driven bandwidths. We propose to approximate the future optimal bandwidths by a sequence of...
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作者：Edwards, David J.; Mee, Robert W.
作者单位：Virginia Commonwealth University; University of Tennessee System; University of Tennessee Knoxville
摘要：Two-level fractional factorial designs are often used in screening scenarios to identify active factors. This article investigates the block diagonal structure of the information matrix of nonregular two-level designs. This structure is appealing since estimates of parameters belonging to different diagonal submatrices are uncorrelated. As such, the covariance matrix of the least squares estimates is simplified and the number of linear dependencies is reduced. We connect the block diagonal inf...
