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作者：Van Werde, Alexander
作者单位：Eindhoven University of Technology; Eindhoven University of Technology
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作者：Wang, Kaizheng
作者单位：Columbia University; Columbia University
摘要：In the 1930s, Psychologists began developing Multiple-Factor Analysis to decompose multivariate data into a small number of interpretable factors without any a priori knowledge about those factors. In this form of factor analysis, the Varimax factor rotation redraws the axes through the multi-dimensional factors to make them sparse and thus make them more interpretable. Charles Spearman and many others objected to factor rotations because the factors seem to be rotationally invariant. Despite ...
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作者：Wang, Guanghui; Feng, Long
作者单位：East China Normal University; East China Normal University; Nankai University; Nankai University
摘要：High-dimensional changepoint inference that adapts to various change patterns has received much attention recently. We propose a simple, fast yet effective approach for adaptive changepoint testing. The key observation is that two statistics based on aggregating cumulative sum statistics over all dimensions and possible changepoints by taking their maximum and summation, respectively, are asymptotically independent under some mild conditions. Hence, we are able to form a new test by combining ...
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作者：Chen, Shuxiao; Zhang, Bo
作者单位：University of Pennsylvania; Fred Hutchinson Cancer Center; Fred Hutchinson Cancer Center
摘要：Estimating dynamic treatment regimes (DTRs) from retrospective observational data is challenging as some degree of unmeasured confounding is often expected. In this work, we develop a framework of estimating properly defined 'optimal' DTRs with a time-varying instrumental variable (IV) when unmeasured covariates confound the treatment and outcome, rendering the potential outcome distributions only partially identified. We derive a novel Bellman equation under partial identification, use it to ...
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作者：Jiang, J.; Wand, M. P.; Bhaskaran, A.
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作者：Ascolani, Filippo; Lijoi, Antonio; Pruenster, Igor
作者单位：Bocconi University
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作者：Whitehouse, Michael; Whiteley, Nick; Rimella, Lorenzo
作者单位：University of Bristol; Lancaster University
摘要：Addressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson approximate likelihood (PAL) methods. In contrast to the popular ordinary differential equation (ODE) approach to compartmental modelling, in which a large population limit is used to motivate a deterministic model, PALs are derived from approximate filtering equations for finite-population, stochastic compartmental models, and the large population limit drives consistency...
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作者：Yang, Shu; Gao, Chenyin; Zeng, Donglin; Wang, Xiaofei
作者单位：North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill; Duke University
摘要：We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data. We characterise the asymptotic distribution of the test-based estimator under local alternat...
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作者：Han, Rungang; Zhang, Anru R.
作者单位：Duke University
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作者：Pargent, Florian; Goretzko, David; von Oertzen, Timo
作者单位：University of Munich; Utrecht University; Max Planck Society; Bundeswehr University Munich; University of Munich
