494期

最新文章

• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models AI翻译
  作者：Fan, Jianqing; Feng, Yang; Song, Rui
  作者单位：Princeton University; Columbia University; Colorado State University System; Colorado State University Fort Collins
  摘要：A variable screening procedure via correlation learning was proposed by Fan and Lv (2008) to reduce dimensionality in sparse ultra-high-dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening (NIS) is a specific type of sure independence screening. We propose several closely related variable screening pro...
• Making and Evaluating Point Forecasts AI翻译
  作者：Gneiting, Tilmann
  作者单位：Ruprecht Karls University Heidelberg
  摘要：Typically, point forecasting methods are compared and assessed by means of an error measure or scoring function, with the absolute error and the squared error being key examples. The individual scores are averaged over forecast cases, to result in a summary measure of the predictive performance, such as the mean absolute error or the mean squared error. I demonstrate that this common practice can lead to grossly misguided inferences, unless the scoring function and the forecasting task are car...
• Malaria in Northwest India: Data Analysis via Partially Observed Stochastic Differential Equation Models Driven by Levy Noise AI翻译
  作者：Bhadra, Anindya; Ionides, Edward L.; Laneri, Karina; Pascual, Mercedes; Bouma, Menno; Dhiman, Ramesh C.
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; Howard Hughes Medical Institute; University of London; London School of Hygiene & Tropical Medicine; Indian Council of Medical Research (ICMR); ICMR - National Institute of Malaria Research (NIMR)
  摘要：Many biological systems are appropriately described by partially observed Markov process (POMP) models, also known as state space models. Such models also arise throughout the physical and social sciences, in engineering, and in finance. Statistical challenges arise in carrying out inference on nonlinear, nonstationary, vector-valued POMP models. Methodologies that depend on the Markov process model only through numerical solution of sample paths are said to have the plug-and-play property. Th...
• Estimating the Term Structure With a Semiparametric Bayesian Hierarchical Model: An Application to Corporate Bonds AI翻译
  作者：Cruz-Marcelo, Alejandro; Ensor, Katherine B.; Rosner, Gary L.
  作者单位：Capital One Financial Corporation; Rice University; Johns Hopkins University; Johns Hopkins University
  摘要：The term structure of interest rates is used to price defaultable bonds and credit derivatives, as well as to infer the quality of bonds for risk management purposes. We introduce a model that jointly estimates term structures by means of a Bayesian hierarchical model with a prior probability model based on Dirichlet process mixtures. The modeling methodology borrows strength across term structures for purposes of estimation. The main advantage of our framework is its ability to produce reliab...
• Estimating the Jump Activity Index Under Noisy Observations Using High-Frequency Data AI翻译
  作者：Jing, Bing-Yi; Kong, Xin-Bing; Liu, Zhi
  作者单位：Hong Kong University of Science & Technology; Lanzhou University; Xiamen University
  摘要：It is widely accepted that the high-frequency data are contaminated by microstructure noise, whose effect on the statistical inference has been of increasing interest in the literature. Much of it, however, has focused on the integrated volatility. In this article, we investigate another important characteristic, namely, the jump activity index (JAI) of a discretely sampled semi-martingale corrupted by microstructure noise. We point out that ignoring the microstructure noise can have a disastr...
• Global Warming and Local Dimming: The Statistical Evidence AI翻译
  作者：Magnus, Jan R.; Melenberg, Bertrand; Muris, Chris
  作者单位：Tilburg University; University of Gottingen
  摘要：Two effects largely determine global warming: the well-known greenhouse effect and the less well-known solar radiation effect. An increase in concentrations of carbon dioxide and other greenhouse gases contributes to global warming: the greenhouse effect. In addition, small particles, called aerosols, reflect and absorb sunlight in the atmosphere. More pollution causes an increase in aerosols, so that less sunlight reaches the Earth (global dimming). Despite its name, global dimming is primari...
• Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression AI翻译
  作者：Fenske, Nora; Kneib, Thomas; Hothorn, Torsten
  作者单位：University of Munich; Carl von Ossietzky Universitat Oldenburg
  摘要：We investigated the risk factors for childhood malnutrition in India based on the 2005/2006 Demographic and Health Survey by applying a novel estimation technique for additive quantile regression. Ordinary linear and generalized linear regression models relate the mean of a response variable to a linear combination of covariate effects, and, as a consequence, focus on average properties of the response. The use of such a regression model for analyzing childhood malnutrition in developing or tr...
• A Constrained l1 Minimization Approach to Sparse Precision Matrix Estimation AI翻译
  作者：Cai, Tony; Liu, Weidong; Luo, Xi
  作者单位：University of Pennsylvania; Shanghai Jiao Tong University
  摘要：This article proposes a constrained l(1) minimization method for estimating a sparse inverse covariance matrix based on a sample of n iid p-variate random variables. The resulting estimator is shown to have a number of desirable properties. In particular, the rate of convergence between the estimator and the true s-sparse precision matrix under the spectral norm is s root logp/n when the population distribution has either exponential-type tails or polynomial-type tails. We present convergence ...
• Outlier Detection Using Nonconvex Penalized Regression AI翻译
  作者：She, Yiyuan; Owen, Art B.
  作者单位：State University System of Florida; Florida State University; Stanford University
  摘要：This article studies the outlier detection problem from the standpoint of penalized regression. In the regression model, we add one mean shift parameter for each of the n data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual L-1 penalty yields a convex criterion, but fails to deliver a robust estimator. The L-1 penalty corresponds to soft thresholding. We introduce a thresholding (denoted by Theta) based iterative procedure for outlier detecti...
• Robust and Efficient One-Way MANOVA Tests AI翻译
  作者：Van Aelst, Stefan; Willems, Gert
  作者单位：Ghent University
  摘要：We propose robust tests as alternatives to the classical Wilks' Lambda test in one-way MANOVA. The robust tests use highly robust and efficient multisample multivariate S-estimators or MM-estimators instead of the empirical covariances. The properties of several robust test statistics are compared. Under the null hypothesis, the distribution of the test statistics is proportional to a chi-square distribution. As an alternative to the asymptotic distribution, we develop a fast robust bootstrap ...