Journal of the Royal Statistical Society: Series B

最新文章

• High dimensional change point estimation via sparse projection AI翻译
  作者：Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge
  摘要：Change points are a very common feature of big data' that arrive in the form of a data stream. We study high dimensional time series in which, at certain time points, the mean structure changes in a sparse subset of the co-ordinates. The challenge is to borrow strength across the co-ordinates to detect smaller changes than could be observed in any individual component series. We propose a two-stage procedure called inspect for estimation of the change points: first, we argue that a good projec...
• Panning for gold: model-X' knockoffs for high dimensional controlled variable selection AI翻译
  作者：Candes, Emmanuel; Fan, Yingying; Janson, Lucas; Lv, Jinchi
  作者单位：Stanford University; University of Southern California
  摘要：Many contemporary large-scale applications involve building interpretable models linking a large set of potential covariates to a response in a non-linear fashion, such as when the response is binary. Although this modelling problem has been extensively studied, it remains unclear how to control the fraction of false discoveries effectively even in high dimensional logistic regression, not to mention general high dimensional non-linear models. To address such a practical problem, we propose a ...
• Full likelihood inference for abundance from continuous time capture-recapture data AI翻译
  作者：Liu, Yang; Liu, Yukun; Li, Pengfei; Qin, Jing
  作者单位：East China Normal University; University of Waterloo; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
  摘要：Capture-recapture experiments are widely used cost-effective sampling techniques for estimating population sizes or abundances in biology, ecology, demography, epidemiology and reliability studies. For continuous time capture-recapture data, existing estimation methods are based on conditional likelihoods and an inverse weighting estimating equation. The corresponding Wald-type confidence intervals for the abundance may have severe undercoverage, and their lower limits can be below the number ...
• False discovery rate control for high dimensional networks of quantile associations conditioning on covariates AI翻译
  作者：Xie, Jichun; Li, Ruosha
  作者单位：Duke University; University of Texas System; University of Texas Health Science Center Houston
  摘要：Motivated by gene coexpression pattern analysis, we propose a novel sample quantile contingency (SQUAC) statistic to infer quantile associations conditioning on covariates. It features enhanced flexibility in handling variables with both arbitrary distributions and complex association patterns conditioning on covariates. We first derive its asymptotic null distribution, and then develop a multiple-testing procedure based on the SQUAC statistic to test simultaneously the independence between on...
• Detecting and dating structural breaks in functional data without dimension reduction AI翻译
  作者：Aue, Alexander; Rice, Gregory; Sonmez, Ozan
  作者单位：University of California System; University of California Davis; University of Waterloo
  摘要：Methodology is proposed to uncover structural breaks in functional data that is fully functional' in the sense that it does not rely on dimension reduction techniques. A thorough asymptotic theory is developed for a fully functional break detection procedure as well as for a break date estimator, assuming a fixed break size and a shrinking break size. The latter result is utilized to derive confidence intervals for the unknown break date. The main results highlight that the fully functional pr...
• Unified empirical likelihood ratio tests for functional concurrent linear models and the phase transition from sparse to dense functional data AI翻译
  作者：Wang, Honglang; Zhong, Ping-Shou; Cui, Yuehua; Li, Yehua
  作者单位：Purdue University System; Purdue University; Purdue University in Indianapolis; Michigan State University; Iowa State University
  摘要：We consider the problem of testing functional constraints in a class of functional concurrent linear models where both the predictors and the response are functional data measured at discrete time points. We propose test procedures based on the empirical likelihood with bias-corrected estimating equations to conduct both pointwise and simultaneous inferences. The asymptotic distributions of the test statistics are derived under the null and local alternative hypotheses, where sparse and dense ...
• Detecting heteroscedasticity in non-parametric regression using weighted empirical processes AI翻译
  作者：Chown, Justin; Mueller, Ursula U.
  作者单位：Ruhr University Bochum; Texas A&M University System; Texas A&M University College Station
  摘要：Heteroscedastic errors can lead to inaccurate statistical conclusions if they are not properly handled. We introduce a test for heteroscedasticity for the non-parametric regression model with multiple covariates. It is based on a suitable residual-based empirical distribution function. The residuals are constructed by using local polynomial smoothing. Our test statistic involves a detection function' that can verify heteroscedasticity by exploiting just the independence-dependence structure be...
• Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects AI翻译
  作者：Shi, Chengchun; Song, Rui; Lu, Wenbin; Fu, Bo
  作者单位：North Carolina State University; Fudan University
  摘要：A salient feature of data from clinical trials and medical studies is inhomogeneity. Patients not only differ in baseline characteristics, but also in the way that they respond to treatment. Optimal individualized treatment regimes are developed to select effective treatments based on patient's heterogeneity. However, the optimal treatment regime might also vary for patients across different subgroups. We mainly consider patients' heterogeneity caused by groupwise individualized treatment effe...
• Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables AI翻译
  作者：Wang, Linbo; Tchetgen, Eric Tchetgen
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Instrumental variables are widely used for estimating causal effects in the presence of unmeasured confounding. Under the standard instrumental variable model, however, the average treatment effect is only partially identifiable. To address this, we propose novel assumptions that enable identification of the average treatment effect. Our identification assumptions are clearly separated from model assumptions that are needed for estimation, so researchers are not required to commit to a specifi...
• A geometric approach to confidence regions and bands for functional parameters AI翻译
  作者：Choi, Hyunphil; Reimherr, Matthew
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Functional data analysis is now a well-established discipline of statistics, with its core concepts and perspectives in place. Despite this, there are still fundamental statistical questions which have received relatively little attention. One of these is the systematic construction of confidence regions for functional parameters. This work is concerned with developing, understanding and visualizing such regions. We provide a general strategy for constructing confidence regions in a real separ...