2020

最新文章

• An information theoretic approach for selecting arms in clinical trials AI翻译
  作者：Mozgunov, Pavel; Jaki, Thomas
  作者单位：Lancaster University; University of Cambridge
  摘要：The question of selecting the 'best' among different choices is a common problem in statistics. In drug development, our motivating setting, the question becomes, for example, which treatment gives the best response rate. Motivated by recent developments in the theory of context-dependent information measures, we propose a flexible response-adaptive experimental design based on a novel criterion governing treatment arm selections which can be used in adaptive experiments with simple (e.g. bina...
• Estimating densities with non-linear support by using Fisher-Gaussian kernels AI翻译
  作者：Mukhopadhyay, Minerva; Li, Didong; Dunson, David B.
  作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur; Duke University
  摘要：Current tools for multivariate density estimation struggle when the density is concentrated near a non-linear subspace or manifold. Most approaches require the choice of a kernel, with the multivariate Gaussian kernel by far the most commonly used. Although heavy-tailed and skewed extensions have been proposed, such kernels cannot capture curvature in the support of the data. This leads to poor performance unless the sample size is very large relative to the dimension of the data. The paper pr...
• Functional models for time-varying random objects AI翻译
  作者：Dubey, Paromita; Mueller, Hans-Georg
  作者单位：University of California System; University of California Davis
  摘要：Functional data analysis provides a popular toolbox of functional models for the analysis of samples of random functions that are real valued. In recent years, samples of time-varying object data such as time-varying networks that are not in a vector space have been increasingly collected. These data can be viewed as elements of a general metric space that lacks local or global linear structure and therefore common approaches that have been used with great success for the analysis of functiona...
• Renewable estimation and incremental inference in generalized linear models with streaming data sets AI翻译
  作者：Luo, Lan; Song, Peter X-K
  作者单位：University of Michigan System; University of Michigan
  摘要：The paper presents an incremental updating algorithm to analyse streaming data sets using generalized linear models. The method proposed is formulated within a new framework of renewable estimation and incremental inference, in which the maximum likelihood estimator is renewed with current data and summary statistics of historical data. Our framework can be implemented within a popular distributed computing environment, known as Apache Spark, to scale up computation. Consisting of two data-pro...
• Superconsistent estimation of points of impact in non-parametric regression with functional predictors AI翻译
  作者：Poss, Dominik; Liebl, Dominik; Kneip, Alois; Eisenbarth, Hedwig; Wager, Tor D.; Barrett, Lisa Feldman
  作者单位：University of Bonn; Victoria University Wellington; Dartmouth College; Northeastern University; Harvard University; Harvard University Medical Affiliates; Massachusetts General Hospital; Harvard University; Harvard Medical School; Harvard University; Harvard University Medical Affiliates; Massachusetts General Hospital
  摘要：Predicting scalar outcomes by using functional predictors is a classical problem in functional data analysis. In many applications, however, only specific locations or time points of the functional predictors have an influence on the outcome. Such 'points of impact' are typically unknown and must be estimated in addition to estimating the usual model components. We show that our points-of-impact estimator enjoys a superconsistent rate of convergence and does not require knowledge or pre-estima...
• Robust estimation via robust gradient estimation AI翻译
  作者：Prasad, Adarsh; Suggala, Arun Sai; Balakrishnan, Sivaraman; Ravikumar, Pradeep
  作者单位：Carnegie Mellon University
  摘要：We provide a new computationally efficient class of estimators for risk minimization. We show that these estimators are robust for general statistical models, under varied robustness settings, including in the classical Huber epsilon-contamination model, and in heavy-tailed settings. Our workhorse is a novel robust variant of gradient descent, and we provide conditions under which our gradient descent variant provides accurate estimators in a general convex risk minimization problem. We provid...
• Visualizing the effects of predictor variables in black box supervised learning models AI翻译
  作者：Apley, Daniel W.; Zhu, Jingyu
  作者单位：Northwestern University
  摘要：In many supervised learning applications, understanding and visualizing the effects of the predictor variables on the predicted response is of paramount importance. A shortcoming of black box supervised learning models (e.g. complex trees, neural networks, boosted trees, random forests, nearest neighbours, local kernel-weighted methods and support vector regression) in this regard is their lack of interpretability or transparency. Partial dependence plots, which are the most popular approach f...
• Report of the Editors-2019 AI翻译
  作者：Dunson, David; Wood, Simon
• Multiply robust causal inference with double-negative control adjustment for categorical unmeasured confounding AI翻译
  作者：Shi, Xu; Miao, Wang; Nelson, Jennifer C.; Tchetgen Tchetgen, Eric J.
  作者单位：University of Michigan System; University of Michigan; Peking University; Kaiser Permanente; University of Pennsylvania
  摘要：Unmeasured confounding is a threat to causal inference in observational studies. In recent years, the use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a long-standing tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao and colleagues have described sufficient conditions under which a pair of negative con...
• Multivariate type G Matern stochastic partial differential equation random fields AI翻译
  作者：Bolin, David; Wallin, Jonas
  作者单位：King Abdullah University of Science & Technology; University of Gothenburg; Lund University
  摘要：For many applications with multivariate data, random-field models capturing departures from Gaussianity within realizations are appropriate. For this reason, we formulate a new class of multivariate non-Gaussian models based on systems of stochastic partial differential equations with additive type G noise whose marginal covariance functions are of Matern type. We consider four increasingly flexible constructions of the noise, where the first two are similar to existing copula-based models. In...