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作者：Zeng, Lang; Tang, Weijing; Ren, Zhao; Ding, Ying
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Carnegie Mellon University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：The stochastic gradient descent (SGD) algorithm has been widely used to optimize deep Cox neural network (Cox-NN) by updating model parameters using mini-batches of data. We show that SGD aims to optimize the average of mini-batch partial-likelihood, which is different from the standard partial-likelihood. This distinction requires developing new statistical properties for the global optimizer, namely, the mini-batch maximum partial-likelihood estimator (mb-MPLE). We establish that mb-MPLE for...
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作者：Chen, Xuanyu; Zhu, Jin; Zhu, Junxian; Wang, Xueqin; Zhang, Heping
作者单位：Sun Yat Sen University; University of Michigan System; University of Michigan; University of Birmingham; National University of Singapore; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Yale University
摘要：The reconstruction of interaction networks between random events is a critical problem arising from statistical physics and politics, sociology, biology, psychology, and beyond. The Ising model lays the foundation for this reconstruction process, but finding the underlying Ising model from the least amount of observed samples in a computationally efficient manner has been historically challenging for half a century. Using sparsity learning, we present an approach named SLIDE whose sample compl...
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作者：Li, Wei; Liu, Jiapeng; Ding, Peng; Geng, Zhi
作者单位：Renmin University of China; Renmin University of China; University of California System; University of California Berkeley; Beijing Technology & Business University
摘要：Estimating causal effects in a target population with unmeasured confounders is challenging, especially when instrumental variables (IVs) are unavailable. However, IVs from auxiliary populations with similar problems can help infer causal effects in the target population. While the homogeneous conditional average treatment effect assumption has been widely used for effect transportability, it has not been explored in IV-based data fusion. We include it as a basic approach, though it may be bia...
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作者：Kaul, Abhishek
作者单位：Washington State University
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作者：Reluga, Katarzyna; Kong, Dehan; Ranjbar, Setareh; Salvati, Nicola; van der Laan, Mark
作者单位：Humboldt University of Berlin; University of Toronto; University of Lausanne; Centre Hospitalier Universitaire Vaudois (CHUV); University of Pisa; University of California System; University of California Berkeley
摘要：Job stability-encompassing secure contracts, adequate wages, social benefits, and career opportunities-is a critical determinant in reducing monetary poverty, as it provides households with reliable income and enhances economic well-being. This study draws on EU-SILC survey and census data to estimate the causal effect of job stability on monetary poverty across Italian provinces, quantifying its influence, and analyzing regional disparities. We introduce a novel causal small area estimation (...
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作者：Zhang, Ruqian; Zhang, Yijiao; Shen, Juan; Zhu, Zhongyi; Qu, Annie
作者单位：Fudan University; University of Pennsylvania; Pennsylvania Medicine; University of California System; University of California Santa Barbara
摘要：The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. In this article, we propose a novel Bayesian transfer learning method named CONCERT to allow robust partial information transfer for high-dimensional data analysis. A condit...
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作者：Hung, Noah Yi-Ting; Lin, Li-Hsiang; Calhoun, Vince D.
作者单位：University System of Georgia; Georgia State University; University System of Georgia; Emory University; Georgia Institute of Technology; Georgia State University
摘要：Deep neural networks (DNNs) have been widely applied to solve real-world regression problems. However, selecting optimal network structures remains a significant challenge. This study addresses this issue by linking neuron selection in DNNs to knot placement in basis expansion techniques. We introduce a difference penalty that automates knot selection, thereby simplifying the complexities of neuron selection. We name this method Deep P-Spline (DPS). This approach extends the class of models co...
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作者：Wang, Weichen; Shi, Chengchun
作者单位：University of Hong Kong; University of London; London School Economics & Political Science
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作者：Li, Yujue; Xue, Fei; Li, Bingxuan; Yang, Yilin; Fan, Zirui; Shu, Juan; Yang, Xiaochen; Wang, Xiyao; Lin, Jinjie; Copana, Carlos; Zhao, Bingxin
作者单位：Purdue University System; Purdue University; Purdue University System; Purdue University; University of Pennsylvania; Yale University
摘要：As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summar...
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作者：Kock, Anders Bredahl; Preinerstorfer, David
作者单位：University of Oxford; Vienna University of Economics & Business
摘要：Tests based on the 2- and infinity-norm have received considerable attention in high-dimensional testing problems, as they are powerful against dense and sparse alternatives, respectively. The power enhancement principle of Fan, Liao, and Yao combines these two norms to construct improved tests that are powerful against both types of alternatives. In the context of testing whether a candidate parameter satisfies a large number of moment equalities, we construct tests that harness the strength ...
