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作者：Zhong, Qixian; Mueller, Jonas; Wang, Jane-Ling
作者单位：Xiamen University; Xiamen University; University of California System; University of California Davis
摘要：Deep learning has become enormously popular in the analysis of complex data, including event time measurements with censoring. To date, deep survival methods have mainly focused on prediction. Such methods are scarcely used in matters of statistical inference such as hypothesis testing. Due to their black-box nature, deep-learned outcomes lack interpretability which limits their use for decision-making in biomedical applications. This article provides estimation and inference methods for the n...
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作者：Rosenbaum, Paul R.; Zubizarreta, Jose R.
作者单位：University of Pennsylvania; Harvard University; Harvard Medical School; Harvard University
摘要：In experimental design, aliasing of effects occurs in fractional factorial experiments, where certain low order factorial effects are indistinguishable from certain high order interactions: low order contrast weights may be orthogonal to one another, while their higher order interactions are aliased and not identified. In observational studies, aliasing occurs when certain combinations of covariates-for example, time period and various eligibility criteria for treatment-perfectly predict the t...
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作者：Sun, Maojun; Han, Ruijian; Jiang, Binyan; Qi, Houduo; Sun, Defeng; Yuan, Yancheng; Huang, Jian
作者单位：Hong Kong Polytechnic University; Hong Kong Polytechnic University; Hong Kong Polytechnic University
摘要：We introduce LArge Model Based Data Agent (LAMBDA), a novel open-source, code-free multi-agent data analysis system that leverages the power of large language models. LAMBDA is designed to address data analysis challenges in data-driven applications through innovatively designed data agents using natural language. At the core of LAMBDA are two key agent roles: the programmer and the inspector, which are engineered to work together seamlessly. Specifically, the programmer generates code based o...
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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：Choi, Jungjun; Kwon, Hyukjun; Liao, Yuan
作者单位：University of Rhode Island; Princeton University; Rutgers University System; Rutgers University New Brunswick
摘要：This article studies the inference about linear functionals of high-dimensional low-rank matrices. While most existing inference methods would require consistent estimation of the true rank, our procedure is robust to rank misspecification, making it a promising approach in applications where rank estimation can be unreliable. We estimate the low-rank spaces using pre-specified weighting matrices, known as diversified projections. A novel statistical insight is that, unlike the usual statistic...
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作者：Perry, Ronan; Panigrahi, Snigdha; Bien, Jacob; Witten, Daniela
作者单位：University of Washington; University of Washington Seattle; University of Michigan System; University of Michigan; University of Southern California; University of Washington; University of Washington Seattle
摘要：Principal component analysis (PCA) is a longstanding approach for dimension reduction. It rests upon the assumption that the underlying signal has low rank, and thus can be well-summarized using a small number of dimensions. The output of PCA is typically represented using a scree plot, which displays the proportion of variance explained (PVE) by each principal component. While the PVE is extensively reported in routine analyses, to the best of our knowledge the notion of inference on the PVE ...
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作者：Kang, Seungwoo; Oh, Hee-Seok
作者单位：Seoul National University (SNU); Seoul National University (SNU)
摘要：A new measure, L-1 centrality, is proposed to assess the centrality of vertices in an undirected and connected graph. The proposed measure can adequately handle graphs with weights assigned to vertices and edges. This study provides tools for graphical and multiscale analysis based on the L-1 centrality. Specifically, the suggested analysis tools include the target plot, L-1 centrality-based neighborhood, and local L-1 centrality. Most importantly, our work is closely associated with the conce...
