Journal of the American Statistical Association

最新文章

• Analyzing Cross-Trait Genetic Architecture with the BIGA Cloud Computing Platform AI翻译
  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...
• Enhanced Power Enhancements for Testing Many Moment Equalities: Beyond the 2-and ∞-norm AI翻译
  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 ...
• Principal Component Analysis for Max-Stable Distributions AI翻译
  作者：Reinbott, Felix; Janssen, Anja
  作者单位：Otto von Guericke University
  摘要：Principal component analysis (PCA) is one of the most popular dimension reduction techniques in statistics and is especially powerful when a multivariate distribution is concentrated near a lower-dimensional subspace. Multivariate extreme value distributions have turned out to provide challenges for the application of PCA since their constraint support impedes the detection of lower-dimensional structures and heavy-tails can imply that second moments do not exist, thereby preventing the applic...
• When Does Bottom-Up Beat Top-Down in Hierarchical Community Detection? AI翻译
  作者：Dreveton, Maximilien; Kuroda, Daichi; Grossglauser, Matthias; Thiran, Patrick
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：Hierarchical community detection consists in finding a tree of communities where deeper levels of the hierarchy reveal finer-grained structures. There are two main classes of algorithms for this task. Divisive (top-down) algorithms recursively partition nodes into smaller communities until a stopping criterion indicates that no further splits are necessary. In contrast, agglomerative (bottom-up) algorithms first identify the smallest community structures and then repeatedly merge the communiti...
• Understanding Inequalities in Cancer Survival Using Bayesian Machine Learning AI翻译
  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...
• A Powerful Transformation of Quantitative Responses for Biobank-Scale Association Studies AI翻译
  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...
• Aggregated Projection Method: A New Approach for Group Factor Model AI翻译
  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...
• Likelihood Ratio Tests in Random Graph Models with Increasing Dimensions AI翻译
  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...
• Estimation of Over-Parameterized Models from an Auto-Modeling Perspective AI翻译
  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...
• Comment on Data Fission: Splitting a Single Data Point Data Fission for Unsupervised Learning: A Discussion on Post-Clustering Inference and the Challenges of Debiasing AI翻译
  作者：Wang, Changhu; Ge, Xinzhou; Song, Dongyuan; Li, Jingyi Jessica
  作者单位：University of California System; University of California Los Angeles; Oregon State University; University of California System; University of California Los Angeles