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作者：Qin, Caihong; Xie, Jinhan; Li, Ting; Bai, Yang
作者单位：Shanghai University of Finance & Economics; Yunnan University
摘要：In this article, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source datasets. To facilitate transfer learning, we propose a novel transferability function tailored for classification problems, enabling a more accurate evaluation of the similarity between source and target dataset distributions. Interestingly, we find that a source dataset can offer more substantial b...
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作者：Gruber, Luis; Kastner, Gregor; Bhattacharya, Anirban; Pati, Debdeep; Pillai, Natesh; Dunson, David
作者单位：University of Klagenfurt; Texas A&M University System; Texas A&M University College Station; University of Wisconsin System; University of Wisconsin Madison; Harvard University; Duke University
摘要：Bhattacharya et al. introduce a novel prior, the Dirichlet-Laplace (DL) prior, and propose a Markov chain Monte Carlo (MCMC) method to simulate posterior draws under this prior in a conditionally Gaussian setting. The original algorithm samples from conditional distributions in the wrong order, that is, it does not correctly sample from the joint posterior distribution of all latent variables. This note details the issue and provides two simple solutions: A correction to the original algorithm...
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作者：Kock, Anders B.; Pedersen, Rasmus S.; Sorensen, Jesper R. -V.
作者单位：University of Oxford; University of Copenhagen; Danish Finance Institute
摘要：Lasso-type estimators are routinely used to estimate high-dimensional time series models. The theoretical guarantees established for these estimators typically require the penalty level to be chosen in a suitable fashion often depending on unknown population quantities. Furthermore, the resulting estimates and the number of variables retained in the model depend crucially on the chosen penalty level. However, there is currently no theoretically founded guidance for this choice in the context o...
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作者：Athreya, Avanti; Lubberts, Zachary; Park, Youngser; Priebe, Carey
作者单位：Johns Hopkins University; University of Virginia; Johns Hopkins University
摘要：Analyzing changes in network evolution is central to statistical network inference. We consider a dynamic network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection probabilities are functions of these vectors. Under mild assumptions, the evolution of latent vectors exhibits low-dimensional manifold structure under a suitable distance. This distance can be approximated by a measure of separation between the observed networks th...
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作者：Chan, Kwun Chuen Gary; Prentice, Ross L.; Yuan, Zhenman
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
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作者：Basak, Piyali; Maringe, Camille; Rubio, F. Javier; Linero, Antonio R.
作者单位：Merck & Company; Merck & Company USA; University of London; London School of Hygiene & Tropical Medicine; University of London; University College London; University of Texas System; University of Texas Austin
摘要：Most cancer patients are diagnosed after the age of 60, often with existing chronic health conditions (comorbidities), that can delay diagnosis and complicate treatment, prognosis, and monitoring. These comorbidities may exacerbate existing sociodemographic inequalities in cancer survival. While much research has focused on how comorbidities affect overall survival, national and international institutions typically prefer the relative survival framework for population-based studies. This frame...
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作者：Liu, Yaowu; Wang, Tianying
作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; Colorado State University System; Colorado State University Fort Collins
摘要：In linear regression models with non-Gaussian errors, transformations of the response variable are widely used in a broad range of applications. Motivated by various genetic association studies, transformation methods for hypothesis testing have received substantial interest. In recent years, the rise of biobank-scale genetic studies, which feature a vast number of participants that could be around half a million, spurred the need for new transformation methods that are both powerful for detec...
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作者：Hu, Jiaqi; Li, Ting; Wang, Xueqin
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Hong Kong Polytechnic University
摘要：Identifying the global factors among grouped data is crucial in the group factor model. In this article, we propose a novel objective function for the task by maximizing the average of correlations between the latent global factors and group factors, solved through the eigen-decomposition of the aggregated projection matrix. Our method is not only computationally efficient but also robust to strongly correlated local factors. We establish the consistency of the global/local factor number estim...
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作者：Yan, Ting; Li, Yuanzhang; Xu, Jinfeng; Yang, Yaning; Zhu, Ji
作者单位：Central China Normal University; George Washington University; City University of Hong Kong; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Michigan System; University of Michigan
摘要：We explore the Wilks phenomena in two random graph models: the beta-model and the Bradley-Terry model. For two increasing dimensional null hypotheses, including a specified null H-0:beta(i)=beta(0)(i) for i=1,...,r and a homogenous null H-0:beta(1)=...=beta(r), we reveal high dimensional Wilks' phenomena that the normalized log-likelihood ratio statistic, [2{l(beta)-l(beta(0))}-r]/(2r)(1/2), converges in distribution to the standard normal distribution as r goes to infinity. Here, l(beta) is t...
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作者：Jiang, Yiran; Liu, Chuanhai
作者单位：Purdue University System; Purdue University
摘要：From a model-building perspective, we propose a paradigm shift for fitting over-parameterized models. Philosophically, the mindset is to fit models to future observations rather than to the observed sample. Technically, given an imputation method to generate future observations, we fit over-parameterized models to these future observations by optimizing an approximation of the desired expected loss function based on its sample counterpart and an adaptive duality function. The required imputati...
