2期

最新文章

• Estimation and clustering in popularity adjusted block model AI翻译
  作者：Noroozi, Majid; Rimal, Ramchandra; Pensky, Marianna
  作者单位：Washington University (WUSTL); Middle Tennessee State University; State University System of Florida; University of Central Florida
  摘要：The paper considers the Popularity Adjusted Block model (PABM) introduced by Sengupta and Chen (Journal of the Royal Statistical Society Series B, 2018, 80, 365-386). We argue that the main appeal of the PABM is the flexibility of the spectral properties of the graph which makes the PABM an attractive choice for modelling networks that appear in biological sciences. We expand the theory of PABM to the case of an arbitrary number of communities which possibly grows with a number of nodes in the...
• Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach AI翻译
  作者：Pu, Hongming; Zhang, Bo
  作者单位：University of Pennsylvania
  摘要：Individualized treatment rules (ITRs) are considered a promising recipe to deliver better policy interventions. One key ingredient in optimal ITR estimation problems is to estimate the average treatment effect conditional on a subject's covariate information, which is often challenging in observational studies due to the universal concern of unmeasured confounding. Instrumental variables (IVs) are widely used tools to infer the treatment effect when there is unmeasured confounding between the ...
• Anchor regression: Heterogeneous data meet causality AI翻译
  作者：Rothenhausler, Dominik; Meinshausen, Nicolai; Buhlmann, Peter; Peters, Jonas
  作者单位：Stanford University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Copenhagen
  摘要：We consider the problem of predicting a response variable from a set of covariates on a data set that differs in distribution from the training data. Causal parameters are optimal in terms of predictive accuracy if in the new distribution either many variables are affected by interventions or only some variables are affected, but the perturbations are strong. If the training and test distributions differ by a shift, causal parameters might be too conservative to perform well on the above task....
• Nonparametric density estimation over complicated domains AI翻译
  作者：Ferraccioli, Federico; Arnone, Eleonora; Finos, Livio; Ramsay, James O.; Sangalli, Laura M.
  作者单位：University of Padua; Polytechnic University of Milan; University of Padua; McGill University
  摘要：We propose a nonparametric method for density estimation over (possibly complicated) spatial domains. The method combines a likelihood approach with a regularization based on a differential operator. We demonstrate the good inferential properties of the method. Moreover, we develop an estimation procedure based on advanced numerical techniques, and in particular making use of finite elements. This ensures high computational efficiency and enables great flexibility. The proposed method efficien...
• On the optimality of randomization in experimental design: How to randomize for minimax variance and design-based inference AI翻译
  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...
• Principal manifold estimation via model complexity selection AI翻译
  作者：Meng, Kun; Eloyan, Ani
  作者单位：Brown University
  摘要：We propose a framework of principal manifolds to model high-dimensional data. This framework is based on Sobolev spaces and designed to model data of any intrinsic dimension. It includes principal component analysis and principal curve algorithm as special cases. We propose a novel method for model complexity selection to avoid overfitting, eliminate the effects of outliers and improve the computation speed. Additionally, we propose a method for identifying the interiors of circle-like curves ...
• On optimal rerandomization designs AI翻译
  作者：Johansson, Per; Rubin, Donald B.; Schultzberg, Marten
  作者单位：Uppsala University; Tsinghua University
  摘要：Blocking is commonly used in randomized experiments to increase efficiency of estimation. A generalization of blocking removes allocations with imbalance in covariate distributions between treated and control units, and then randomizes within the remaining set of allocations with balance. This idea of rerandomization was formalized by Morgan and Rubin (Annals of Statistics, 2012, 40, 1263-1282), who suggested using Mahalanobis distance between treated and control covariate means as the criteri...
• Finite sample change point inference and identification for high-dimensional mean vectors AI翻译
  作者：Yu, Mengjia; Chen, Xiaohui
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Cumulative sum (CUSUM) statistics are widely used in the change point inference and identification. For the problem of testing for existence of a change point in an independent sample generated from the mean-shift model, we introduce a Gaussian multiplier bootstrap to calibrate critical values of the CUSUM test statistics in high dimensions. The proposed bootstrap CUSUM test is fully data dependent and it has strong theoretical guarantees under arbitrary dependence structures and mild moment c...
• Iterative Alpha Expansion for estimating gradient-sparse signals from linear measurements AI翻译
  作者：Xu, Sheng; Fan, Zhou
  作者单位：Yale University
  摘要：We consider estimating a piecewise-constant image, or a gradient-sparse signal on a general graph, from noisy linear measurements. We propose and study an iterative algorithm to minimize a penalized least-squares objective, with a penalty given by the l0-norm of the signal's discrete graph gradient. The method uses a non-convex variant of proximal gradient descent, applying the alpha-expansion procedure to approximate the proximal mapping in each iteration, and using a geometric decay of the p...