6期

最新文章

• CONVERGENCE RATES OF PARAMETER ESTIMATION FOR SOME WEAKLY IDENTIFIABLE FINITE MIXTURES AI翻译
  作者：Ho, Nhat; Nguyen, Xuanlong
  作者单位：University of Michigan System; University of Michigan
  摘要：We establish minimax lower bounds and maximum likelihood convergence rates of parameter estimation for mean-covariance multivariate Gaussian mixtures, shape-rate Gamma mixtures and some variants of finite mixture models, including the setting where the number of mixing components is bounded but unknown. These models belong to what we call weakly identifiable classes, which exhibit specific interactions among mixing parameters driven by the algebraic structures of the class of kernel densities ...
• DISCUSSION OF INFLUENTIAL FEATURES PCA FOR HIGH DIMENSIONAL CLUSTERING AI翻译
  作者：Arias-Castro, Ery; Verzelen, Nicolas
  作者单位：University of California System; University of California San Diego; INRAE
• HIGH-DIMENSIONAL GENERALIZATIONS OF ASYMMETRIC LEAST SQUARES REGRESSION AND THEIR APPLICATIONS AI翻译
  作者：Gu, Yuwen; Zou, Hui
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：Asymmetric least squares regression is an important method that has wide applications in statistics, econometrics and finance. The existing work on asymmetric least squares only considers the traditional low dimension and large sample setting. In this paper, we systematically study the Sparse Asymmetric LEast Squares (SALES) regression under high dimensions where the penalty functions include the Lasso and nonconvex penalties. We develop a unified efficient algorithm for fitting SALES and esta...
• CAUSAL INFERENCE WITH A GRAPHICAL HIERARCHY OF INTERVENTIONS AI翻译
  作者：Shpitser, Ilya; Tchetgen, Eric Tchetgen
  作者单位：Johns Hopkins University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Identifying causal parameters from observational data is fraught with subtleties due to the issues of selection bias and confounding. In addition, more complex questions of interest, such as effects of treatment on the treated and mediated effects may not always be identified even in data where treatment assignment is known and under investigator control, or may be identified under one causal model but not another. Increasingly complex effects of interest, coupled with a diversity of causal mo...
• REJOINDER: INFLUENTIAL FEATURES PCA FOR HIGH DIMENSIONAL CLUSTERING AI翻译
  作者：Jin, Jiashun; Wang, Wanjie
  作者单位：Carnegie Mellon University; National University of Singapore
• DISCUSSION OF INFLUENTIAL FEATURE PCA FOR HIGH DIMENSIONAL CLUSTERING AI翻译
  作者：Cai, T. Tony; Zhang, Linjun
  作者单位：University of Pennsylvania
• CONSISTENT MODEL SELECTION CRITERIA FOR QUADRATICALLY SUPPORTED RISKS AI翻译
  作者：Kim, Yongdai; Jeon, Jong-June
  作者单位：Seoul National University (SNU); University of Seoul
  摘要：In this paper, we study asymptotic properties of model selection criteria for high-dimensional regression models where the number of covariates is much larger than the sample size. In particular, we consider a class of loss functions calIed the class of quadratically supported risks which is large enough to include the quadratic loss, Huber loss, quantile loss and logistic loss. We provide sufficient conditions for the model selection criteria, which are applicable to the class of quadraticall...
• NONPARAMETRIC ESTIMATION OF DYNAMICS OF MONOTONE TRAJECTORIES AI翻译
  作者：Paul, Debashis; Peng, Jie; Burman, Prabir
  作者单位：University of California System; University of California Davis
  摘要：We study a class of nonlinear nonparametric inverse problems. Specifically, we propose a nonparametric estimator of the dynamics of a monotonically increasing trajectory defined on a finite time interval. Under suitable regularity conditions, we show that in terms of L-2-loss, the optimal rate of convergence for the proposed estimator is the same as that for the estimation of the derivative of a function. We conduct simulation studies to examine the finite sample behavior of the proposed estim...
• FAITHFUL VARIABLE SCREENING FOR HIGH-DIMENSIONAL CONVEX REGRESSION AI翻译
  作者：Xu, Min; Chen, Minhua; Lafferty, John
  作者单位：University of Pennsylvania; Amazon.com; University of Chicago
  摘要：We study the problem of variable selection in convex nonparametric regression. Under the assumption that the true regression function is convex and sparse, we develop a screening procedure to select a subset of variables that contains the relevant variables. Our approach is a two-stage quadratic programming method that estimates a sum of one-dimensional convex functions, followed by one-dimensional concave regression fits on the residuals. In contrast to previous methods for sparse additive mo...
• GLOBAL RATES OF CONVERGENCE IN LOG-CONCAVE DENSITY ESTIMATION AI翻译
  作者：Kim, Arlene K. H.; Samworth, Richard J.
  作者单位：University of Cambridge
  摘要：The estimation of a log-concave density on R-d represents a central problem in the area of nonparametric inference under shape constraints. In this paper, we study the performance of log-concave density estimators with respect to global loss functions, and adopt a minimax approach. We first show that no statistical procedure based on a sample of size n can estimate a log-concave density with respect to the squared Hellinger loss function with supremum risk smaller than order n(-4/5), when d = ...