5期

最新文章

• Empirical likelihood-based inference for functional means with application to wearable device data AI翻译
  作者：Chang, Hsin-wen; McKeague, Ian W.
  作者单位：Academia Sinica - Taiwan; Columbia University
  摘要：This paper develops a nonparametric inference framework that is applicable to occupation time curves derived from wearable device data. These curves consider all activity levels within the range of device readings, which is preferable to the practice of classifying activity into discrete categories. Motivated by certain features of these curves, we introduce a powerful likelihood ratio approach to construct confidence bands and compare functional means. Notably, our approach allows discontinui...
• Conditional independence testing in Hilbert spaces with applications to functional data analysis AI翻译
  作者：Lundborg, Anton Rask; Shah, Rajen D.; Peters, Jonas
  作者单位：University of Cambridge; University of Copenhagen
  摘要：We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables. This generalises testing the significance of X in a regression model of scalar response Y on functional regressors X and Z. We show, however, that even in the idealised setting where additionally (X, Y, Z) has a Gaussian distribution, the power of any test cannot exceed its size. Further modelling assumptions are needed and we argu...
• ZAP: Z$$ Z $$-value adaptive procedures for false discovery rate control with side information AI翻译
  作者：Leung, Dennis; Sun, Wenguang
  作者单位：University of Melbourne; Zhejiang University
  摘要：Adaptive multiple testing with covariates is an important research direction that has gained major attention in recent years. It has been widely recognised that leveraging side information provided by auxiliary covariates can improve the power of false discovery rate (FDR) procedures. Currently, most such procedures are devised with p-values as their main statistics. However, for two-sided hypotheses, the usual data processing step that transforms the primary statistics, known as z-values, int...
• Modelling the COVID-19 infection trajectory: A piecewise linear quantile trend model* AI翻译
  作者：Jiang, Feiyu; Zhao, Zifeng; Shao, Xiaofeng
  作者单位：Fudan University; University of Notre Dame; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We propose a piecewise linear quantile trend model to analyse the trajectory of the COVID-19 daily new cases (i.e. the infection curve) simultaneously across multiple quantiles. The model is intuitive, interpretable and naturally captures the phase transitions of the epidemic growth rate via change-points. Unlike the mean trend model and least squares estimation, our quantile-based approach is robust to outliers, captures heteroscedasticity (commonly exhibited by COVID-19 infection curves) and...
• A statistical test to reject the structural interpretation of a latent factor model AI翻译
  作者：VanderWeele, Tyler J.; Vansteelandt, Stijn
  作者单位：Harvard University; Harvard University; Ghent University
  摘要：Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying construct. When evidence suggests that a single factor is adequate, research often proceeds by using a univariate summary of the indicators in subsequent research. Implicit in such practices is the assumption that it is the underlying latent, rather than the indicators, that is causally efficacious. The assumption that...
• Structure learning for extremal tree models AI翻译
  作者：Engelke, Sebastian; Volgushev, Stanislav
  作者单位：University of Geneva; University of Toronto; University of Geneva
  摘要：Extremal graphical models are sparse statistical models for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation of the complex extremal dependence structure. For the important case of tree models, we develop a data-driven methodology for learning the graphical structure. We show that sample versions of the extremal correlation and a new summary statistic, which we call the extremal variogram, can be used as weights for a mini...
• Segmenting time series via self-normalisation AI翻译
  作者：Zhao, Zifeng; Jiang, Feiyu; Shao, Xiaofeng
  作者单位：University of Notre Dame; Fudan University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We propose a novel and unified framework for change-point estimation in multivariate time series. The proposed method is fully non-parametric, robust to temporal dependence and avoids the demanding consistent estimation of long-run variance. One salient and distinct feature of the proposed method is its versatility, where it allows change-point detection for a broad class of parameters (such as mean, variance, correlation and quantile) in a unified fashion. At the core of our method, we couple...
• Causal inference with spatio-temporal data: Estimating the effects of airstrikes on insurgent violence in Iraq AI翻译
  作者：Papadogeorgou, Georgia; Imai, Kosuke; Lyall, Jason; Li, Fan
  作者单位：State University System of Florida; University of Florida; Harvard University; Harvard University; Dartmouth College; Duke University; State University System of Florida; University of Florida
  摘要：Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio-temporal point processes. We extend the potential outcomes framework to these settings by formulating the treatment point process as a stochastic intervention. Our causal estimands include the expected number of outcome events in a specified area under a particular stochastic treatment assignment strat...
• High-dimensional principal component analysis with heterogeneous missingness AI翻译
  作者：Zhu, Ziwei; Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge; University of Michigan System; University of Michigan; University of London; London School Economics & Political Science
  摘要：We study the problem of high-dimensional Principal Component Analysis (PCA) with missing observations. In a simple, homogeneous observation model, we show that an existing observed-proportion weighted (OPW) estimator of the leading principal components can (nearly) attain the minimax optimal rate of convergence, which exhibits an interesting phase transition. However, deeper investigation reveals that, particularly in more realistic settings where the observation probabilities are heterogeneou...
• Dimension-free mixing for high-dimensional Bayesian variable selection AI翻译
  作者：Zhou, Quan; Yang, Jun; Vats, Dootika; Roberts, Gareth O.; Rosenthal, Jeffrey S.
  作者单位：Texas A&M University System; Texas A&M University College Station; University of Oxford; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur; University of Warwick; University of Toronto
  摘要：Yang et al. proved that the symmetric random walk Metropolis-Hastings algorithm for Bayesian variable selection is rapidly mixing under mild high-dimensional assumptions. We propose a novel Markov chain Monte Carlo (MCMC) sampler using an informed proposal scheme, which we prove achieves a much faster mixing time that is independent of the number of covariates, under the assumptions of Yang et al. To the best of our knowledge, this is the first high-dimensional result which rigorously shows th...