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作者：Kim, Kwang-Rae; Dryden, Ian L.; Le, Huiling; Severn, Katie E.
作者单位：University of Nottingham
摘要：There has been increasing interest in statistical analysis of data lying in manifolds. This paper generalizes a smoothing spline fitting method to Riemannian manifold data based on the technique of unrolling, unwrapping and wrapping originally proposed by Jupp and Kent for spherical data. In particular, we develop such a fitting procedure for shapes of configurations in general m-dimensional Euclidean space, extending our previous work for two-dimensional shapes. We show that parallel transpor...
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作者：Wei, Bo; Peng, Limin; Zhang, Mei-Jie; Fine, Jason P.
作者单位：Emory University; Medical College of Wisconsin; University of North Carolina; University of North Carolina Chapel Hill
摘要：The causal effect of a treatment is of fundamental interest in the social, biological and health sciences. Instrumental variable (IV) methods are commonly used to determine causal treatment effects in the presence of unmeasured confounding. In this work, we study a new binary IV framework with randomly censored outcomes where we propose to quantify the causal treatment effect by the concept of complier quantile causal effect (CQCE). The CQCE is identifiable under weaker conditions than the com...
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作者：Delaigle, Aurore; Wood, Simon
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作者：Rossell, David; Abril, Oriol; Bhattacharya, Anirban
作者单位：Pompeu Fabra University; Texas A&M University System; Texas A&M University College Station
摘要：We propose the approximate Laplace approximation (ALA) to evaluate integrated likelihoods, a bottleneck in Bayesian model selection. The Laplace approximation (LA) is a popular tool that speeds up such computation and equips strong model selection properties. However, when the sample size is large or one considers many models the cost of the required optimizations becomes impractical. ALA reduces the cost to that of solving a least-squares problem for each model. Further, it enables efficient ...
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作者：Dubey, Paromita; Mueller, Hans-Georg
作者单位：University of Southern California; University of California System; University of California Davis
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作者：Cai, Tianxi; Cai, T. Tony; Guo, Zijian
作者单位：Harvard University; University of Pennsylvania; Rutgers University System; Rutgers University New Brunswick
摘要：The ability to predict individualized treatment effects (ITEs) based on a given patient's profile is essential for personalized medicine. We propose a hypothesis testing approach to choosing between two potential treatments for a given individual in the framework of high-dimensional linear models. The methodological novelty lies in the construction of a debiased estimator of the ITE and establishment of its asymptotic normality uniformly for an arbitrary future high-dimensional observation, wh...
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作者：Kallus, Nathan
作者单位：Cornell University
摘要：I study the minimax-optimal design for a two-arm controlled experiment where conditional mean outcomes vary in a given set and the objective is effect-estimation precision. When this set is permutation symmetric, the optimal design is shown to be complete randomization. Notably, even when the set has structure (i.e., is not permutation symmetric), being minimax-optimal for precision still requires randomization beyond a single partition of units, that is, beyond randomizing the identity of tre...
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作者：Mourtada, Jaouad; Gaiffas, Stephane; Scornet, Erwan
作者单位：Institut Polytechnique de Paris; Ecole Polytechnique; Centre National de la Recherche Scientifique (CNRS); Centre National de la Recherche Scientifique (CNRS); Universite Paris Cite; Universite PSL; Ecole Normale Superieure (ENS); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI)
摘要：Random forest (RF) is one of the algorithms of choice in many supervised learning applications, be it classification or regression. The appeal of such tree-ensemble methods comes from a combination of several characteristics: a remarkable accuracy in a variety of tasks, a small number of parameters to tune, robustness with respect to features scaling, a reasonable computational cost for training and prediction, and their suitability in high-dimensional settings. The most commonly used RF varia...
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作者：Henzi, Alexander; Ziegel, Johanna F.; Gneiting, Tilmann
作者单位：University of Bern; Heidelberg Institute for Theoretical Studies; Helmholtz Association; Karlsruhe Institute of Technology
摘要：Isotonic distributional regression (IDR) is a powerful non-parametric technique for the estimation of conditional distributions under order restrictions. In a nutshell, IDR learns conditional distributions that are calibrated, and simultaneously optimal relative to comprehensive classes of relevant loss functions, subject to isotonicity constraints in terms of a partial order on the covariate space. Non-parametric isotonic quantile regression and non-parametric isotonic binary regression emerg...
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作者：Salvati, N.; Fabrizi, E.; Ranalli, M. G.; Chambers, R. L.
作者单位：University of Pisa; Catholic University of the Sacred Heart; University of Perugia; University of Wollongong
摘要：Data linkage can be used to combine values of the variable of interest from a national survey with values of auxiliary variables obtained from another source, such as a population register, for use in small area estimation. However, linkage errors can induce bias when fitting regression models; moreover, they can create non-representative outliers in the linked data in addition to the presence of potential representative outliers. In this paper, we adopt a secondary analyst's point of view, as...
