2期

最新文章

• Sparse additive regression on a regular lattice AI翻译
  作者：Abramovich, Felix; Lahav, Tal
  作者单位：Tel Aviv University
  摘要：We consider estimation in a sparse additive regression model with the design points on a regular lattice. We establish the minimax convergence rates over Sobolev classes and propose a Fourier-based rate optimal estimator which is adaptive to the unknown sparsity and smoothness of the response function. The estimator is derived within a Bayesian formalism but can be naturally viewed as a penalized maximum likelihood estimator with the complexity penalties on the number of non-zero univariate ad...
• Asymptotic permutation tests in general factorial designs AI翻译
  作者：Pauly, Markus; Brunner, Edgar; Konietschke, Frank
  作者单位：Heinrich Heine University Dusseldorf; University of Gottingen; UNIVERSITY GOTTINGEN HOSPITAL
  摘要：In general factorial designs where no homoscedasticity or a particular error distribution is assumed, the well-known Wald-type statistic is a simple asymptotically valid procedure. However, it is well known that it suffers from a poor finite sample approximation since the convergence to its (2) limit distribution is quite slow. This becomes even worse with an increasing number of factor levels. The aim of the paper is to improve the small sample behaviour of the Wald-type statistic, maintainin...
• Semiparametric transformation models for causal inference in time-to-event studies with all-or-nothing compliance AI翻译
  作者：Yu, Wen; Chen, Kani; Sobel, Michael E.; Ying, Zhiliang
  作者单位：Fudan University; Hong Kong University of Science & Technology; Columbia University
  摘要：We consider causal inference in randomized survival studies with right-censored outcomes and all-or-nothing compliance, using semiparametric transformation models to estimate the distribution of survival times in treatment and control groups, conditionally on covariates and latent compliance type. Estimands depending on these distributions, e.g. the complier average causal effect, the complier effect on survival beyond time t and the complier quantile effect, are then considered. Maximum likel...
• Multiple-change-point detection for high dimensional time series via sparsified binary segmentation AI翻译
  作者：Cho, Haeran; Fryzlewicz, Piotr
  作者单位：University of Bristol; University of London; London School Economics & Political Science
  摘要：Time series segmentation, which is also known as multiple-change-point detection, is a well-established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second-order structure of a high dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine cumulative sum statistics obtained from local periodograms and cross-periodograms of th...
• Dynamic functional principal components AI翻译
  作者：Hormann, Siegfried; Kidzinski, Lukasz; Hallin, Marc
  作者单位：Universite Libre de Bruxelles; Princeton University
  摘要：We address the problem of dimension reduction for time series of functional data (Xt:tZ). Such functional time series frequently arise, for example, when a continuous time process is segmented into some smaller natural units, such as days. Then each X-t represents one intraday curve. We argue that functional principal component analysis, though a key technique in the field and a benchmark for any competitor, does not provide an adequate dimension reduction in a time series setting. Functional ...
• Doubly robust estimation of the local average treatment effect curve AI翻译
  作者：Ogburn, Elizabeth L.; Rotnitzky, Andrea; Robins, James M.
  作者单位：Johns Hopkins University; Universidad Torcuato Di Tella; Harvard University
  摘要：We consider estimation of the causal effect of a binary treatment on an outcome, conditionally on covariates, from observational studies or natural experiments in which there is a binary instrument for treatment. We describe a doubly robust, locally efficient estimator of the parameters indexing a model for the local average treatment effect conditionally on covariates V when randomization of the instrument is only true conditionally on a high dimensional vector of covariates X, possibly bigge...
• Inference for non-stationary time series regression with or without inequality constraints AI翻译
  作者：Zhou, Zhou
  作者单位：University of Toronto
  摘要：We consider statistical inference for time series linear regression where the response and predictor processes may experience general forms of abrupt and smooth non-stationary behaviours over time. Meanwhile, the regression parameters may be subject to linear inequality constraints. A simple and unified procedure for structural stability checks and parameter inference is proposed. In the case where the regression parameters are constrained, the methodology proposed is shown to be consistent wh...
• Estimation of the marginal expected shortfall: the mean when a related variable is extreme AI翻译
  作者：Cai, Juan-Juan; Einmahl, John H. J.; de Haan, Laurens; Zhou, Chen
  作者单位：Delft University of Technology; Tilburg University; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam; Universidade de Lisboa; European Central Bank; De Nederlandsche Bank NV; Tinbergen Institute
  摘要：Denote the loss return on the equity of a financial institution as X and that of the entire market as Y. For a given very small value of p>0, the marginal expected shortfall (MES) is defined as E{X|Y>QY(1-p)}, where Q(Y)(1-p) is the (1-p)th quantile of the distribution of Y. The MES is an important factor when measuring the systemic risk of financial institutions. For a wide non-parametric class of bivariate distributions, we construct an estimator of the MES and establish the asymptotic norma...