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• Robust causal inference with continuous instruments using the local instrumental variable curve AI翻译
  作者：Kennedy, Edward H.; Lorch, Scott; Small, Dylan S.
  作者单位：Carnegie Mellon University; University of Pennsylvania
  摘要：Instrumental variables are commonly used to estimate effects of a treatment afflicted by unmeasured confounding, and in practice instruments are often continuous (e.g. measures of distance, or treatment preference). However, available methods for continuous instruments have important limitations: they either require restrictive parametric assumptions for identification, or else rely on modelling both the outcome and the treatment process well (and require modelling effect modification by all a...
• Report of the Editors2018 AI翻译
  作者：Dunson, David; Wood, Simon
• An omnibus non-parametric test of equality in distribution for unknown functions AI翻译
  作者：Luedtke, Alex; Carone, Marco; van der Laan, Mark J.
  作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of California System; University of California Berkeley
  摘要：We present a novel family of non-parametric omnibus tests of the hypothesis that two unknown but estimable functions are equal in distribution when applied to the observed data structure. We developed these tests, which represent a generalization of the maximum mean discrepancy tests described by Gretton and colleagues, using recent developments from the higher order pathwise differentiability literature. Despite their complex derivation, the associated test statistics can be expressed quite s...
• Multiple testing with the structure-adaptive Benjamini-Hochberg algorithm AI翻译
  作者：Li, Ang; Barber, Rina Foygel
  作者单位：University of Chicago
  摘要：In multiple-testing problems, where a large number of hypotheses are tested simultaneously, false discovery rate (FDR) control can be achieved with the well-known Benjamini-Hochberg procedure, which a(0,1]dapts to the amount of signal in the data, under certain distributional assumptions. Many modifications of this procedure have been proposed to improve power in scenarios where the hypotheses are organized into groups or into a hierarchy, as well as other structured settings. Here we introduc...
• Characterization of c-, L- and φk-optimal designs for a class of non-linear multiple-regression models AI翻译
  作者：Schmidt, Dennis
  作者单位：Otto von Guericke University
  摘要：Optimal designs for multiple-regression models are determined. We consider a general class of non-linear models including proportional hazards models with different censoring schemes, the Poisson and the negative binomial model. For these models we provide a complete characterization of c-optimal designs for all vectors c in the case of a single covariate. For multiple regression with an arbitrary number of covariates, c-optimal designs for certain vectors c are derived analytically. Using som...
• On cross-validation for sparse reduced rank regression AI翻译
  作者：She, Yiyuan; Hoang Tran
  作者单位：State University System of Florida; Florida State University
  摘要：In high dimensional data analysis, regularization methods pursuing sparsity and/or low rank have received much attention recently. To provide a proper amount of shrinkage, it is typical to use a grid search and a model comparison criterion to find the optimal regularization parameters. However, we show that fixing the parameters across all folds may result in an inconsistency issue, and it is more appropriate to cross-validate projection-selection patterns to obtain the best coefficient estima...
• Moment conditions and Bayesian non-parametrics AI翻译
  作者：Bornn, Luke; Shephard, Neil; Solgi, Reza
  作者单位：Simon Fraser University; Harvard University
  摘要：Models phrased through moment conditions are central to much of modern inference. Here these moment conditions are embedded within a non-parametric Bayesian set-up. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools by using Hausdorff measures to analyse them on real and simulated data. These new methods, which involve simulating on a manifold, can be applied w...
• Deterministic parallel analysis: an improved method for selecting factors and principal components AI翻译
  作者：Dobriban, Edgar; Owen, Art B.
  作者单位：University of Pennsylvania
  摘要：Factor analysis and principal component analysis are used in many application areas. The first step, choosing the number of components, remains a serious challenge. Our work proposes improved methods for this important problem. One of the most popular state of the art methods is parallel analysis (PA), which compares the observed factor strengths with simulated strengths under a noise-only model. The paper proposes improvements to PA. We first derandomize it, proposing deterministic PA, which ...