529期

最新文章

• PUlasso: High-Dimensional Variable Selection With Presence-Only Data AI翻译
  作者：Song, Hyebin; Raskutti, Garvesh
  作者单位：University of Wisconsin System; University of Wisconsin Madison
  摘要：In various real-world problems, we are presented with classification problems with positive and unlabeled data, referred to as presence-only responses. In this article we study variable selection in the context of presence only responses where the number of features or covariates p is large. The combination of presence-only responses and high dimensionality presents both statistical and computational challenges. In this article, we develop the PUlasso algorithm for variable selection and class...
• Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity AI翻译
  作者：Ando, Tomohiro; Bai, Jushan
  作者单位：University of Melbourne; Columbia University; Nankai University
  摘要：This article introduces a new procedure for analyzing the quantile co-movement of a large number of financial time series based on a large-scale panel data model with factor structures. The proposed method attempts to capture the unobservable heterogeneity of each of the financial time series based on sensitivity to explanatory variables and to the unobservable factor structure. In our model, the dimension of the common factor structure varies across quantiles, and the explanatory variables is...
• Simulating Copulas: Stochastic Models, Sampling Algorithms, and Applications, 2nd edition AI翻译
  作者：Vatter, Thibault
  作者单位：Columbia University
• From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation: Rejoinder AI翻译
  作者：Rosset, Saharon; Tibshirani, Ryan J.
  作者单位：Tel Aviv University; Carnegie Mellon University; Carnegie Mellon University
• From Distance Correlation to Multiscale Graph Correlation AI翻译
  作者：Shen, Cencheng; Priebe, Carey E.; Vogelstein, Joshua T.
  作者单位：University of Delaware; Johns Hopkins University; Johns Hopkins University; Johns Hopkins University
  摘要：Understanding and developing a correlation measure that can detect general dependencies is not only imperative to statistics and machine learning, but also crucial to general scientific discovery in the big data age. In this paper, we establish a new framework that generalizes distance correlation (Dcorr)-a correlation measure that was recently proposed and shown to be universally consistent for dependence testing against all joint distributions of finite moments-to the multiscale graph correl...
• Adaptive Huber Regression AI翻译
  作者：Sun, Qiang; Zhou, Wen-Xin; Fan, Jianqing
  作者单位：University of Toronto; University of California System; University of California San Diego; Fudan University; Princeton University
  摘要：Big data can easily be contaminated by outliers or contain variables with heavy-tailed distributions, which makes many conventional methods inadequate. To address this challenge, we propose the adaptive Huber regression for robust estimation and inference. The key observation is that the robustification parameter should adapt to the sample size, dimension and moments for optimal tradeoff between bias and robustness. Our theoretical framework deals with heavy-tailed distributions with bounded t...
• Estimation of the Boundary of a Variable Observed With Symmetric Error AI翻译
  作者：Florens, Jean-Pierre; Simar, Leopold; Van Keilegom, Ingrid
  作者单位：Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics; Universite Catholique Louvain; KU Leuven
  摘要：Consider the model with , where tau is an unknown constant (the boundary of X), Z is a random variable defined on , epsilon is a symmetric error, and epsilon and Z are independent. Based on an iid sample of Y, we aim at identifying and estimating the boundary tau when the law of epsilon is unknown (apart from symmetry) and in particular its variance is unknown. We propose an estimation procedure based on a minimal distance approach and by making use of Laguerre polynomials. Asymptotic results ...
• D-CCA: A Decomposition-Based Canonical Correlation Analysis for High-Dimensional Datasets AI翻译
  作者：Shu, Hai; Wang, Xiao; Zhu, Hongtu
  作者单位：University of Texas System; UTMD Anderson Cancer Center; Purdue University System; Purdue University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：A typical approach to the joint analysis of two high-dimensional datasets is to decompose each data matrix into three parts: a low-rank common matrix that captures the shared information across datasets, a low-rank distinctive matrix that characterizes the individual information within a single dataset, and an additive noise matrix. Existing decomposition methods often focus on the orthogonality between the common and distinctive matrices, but inadequately consider the more necessary orthogona...
• Theory of Stochastic Objects: Probability, Stochastic Processes and Inference. AI翻译
  作者：Behme, Anita D.
  作者单位：Technische Universitat Dresden
• Measuring Agreement: Models, Methods, and Applications. AI翻译
  作者：Ismail, Noor Azina
  作者单位：Universiti Malaya