Journal of the Royal Statistical Society: Series B

最新文章

• Stratum order-of-addition designs AI翻译
  作者：Zhou, Liushan; Liu, Ze; Liu, Min-Qian; Chen, Guanzhou
  作者单位：Nankai University
  摘要：Order-of-addition experiments are widely employed in many fields of science and industry to study how the order of components being added influences the response. Although several classes of attractive order-of-addition designs have been proposed in recent years, the performance of these designs often relies on prespecified models and their run sizes are typically large unless the number of components is very small. In this paper, we put forward a model-free approach, called stratum order-of-a...
• Kiho Park, Yo Joong Choe, and Yibo Jiang's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Park, Kiho; Choe, Yo Joong; Jiang, Yibo
  作者单位：University of Chicago; INSEAD Business School; University of Chicago
• Pitman efficiency lower bounds for multivariate distribution-free tests based on optimal transport AI翻译
  作者：Deb, Nabarun; Bhattacharya, Bhaswar B.; Sen, Bodhisattva
  作者单位：University of Chicago; University of Pennsylvania; Columbia University
  摘要：The Wilcoxon rank sum test is one of the most popular distribution-free two-sample tests for univariate data. Among the important reasons for their popularity are the striking results of Hodges-Lehmann and Chernoff-Savage, where the authors show that the asymptotic (Pitman) relative efficiency of Wilcoxon's test compared to Student's t-test, never falls below 0.864 (with identity score) and 1 (with Gaussian score), respectively. Motivated by these results, we propose and study a large family o...
• Penalized empirical likelihood over decentralized networks AI翻译
  作者：Du, Jinye; Wang, Qihua
  作者单位：Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
  摘要：Empirical likelihood encounters serious computational challenges when applied to massive datasets or multiple data sources distributed across decentralized networks. This paper proposes a constrained empirical likelihood framework for decentralized networks, utilizing a novel penalization technique to obtain a penalized empirical log-likelihood. The resulting empirical log-likelihood ratio statistic is proved to be asymptotically standard chi-squared even for a divergent machine number. Howeve...
• Censored quantile regression with time-dependent covariates AI翻译
  作者：Chu, Chi Wing; Sit, Tony; Ying, Zhiliang
  作者单位：City University of Hong Kong; Chinese University of Hong Kong; Columbia University
  摘要：We propose a new class of censored quantile regression models with time-dependent covariates for right-censored failure time data. While time-dependent covariates naturally arise in time-to-event analysis, existing works in the literature discuss treatments for data collected either under an independent censoring mechanism or a longitudinal setting. Our formulation extends the current scope so that the conventional setting of time-dependent covariates can be properly handled. The new framework...
• Estimating maximal symmetries of regression functions via subgroup lattices AI翻译
  作者：Christie, Louis G.; Aston, John A. D.
  作者单位：University of Cambridge
  摘要：We present a method for estimating the maximal symmetry of a continuous regression function. Knowledge of such a symmetry can be used to significantly improve modelling by removing the modes of variation resulting from the symmetries. Symmetry estimation is carried out using hypothesis testing for invariance strategically over the subgroup lattice of a search group G acting on the feature space. We show that the estimation of the unique largest invariant subgroup of G generalizes useful tools ...
• Bayesian penalized empirical likelihood and Markov Chain Monte Carlo sampling AI翻译
  作者：Chang, Jinyuan; Tang, Cheng Yong; Zhu, Yuanzheng
  作者单位：Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Nanjing Institute of Geology & Paleontology, CAS; Academy of Mathematics & System Sciences, CAS; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Southwestern University of Finance & Economics - China
  摘要：In this study, we introduce a novel methodological framework called Bayesian penalized empirical likelihood (BPEL), designed to address the computational challenges inherent in empirical likelihood (EL) approaches. Our approach has two primary objectives: (i) to enhance the inherent flexibility of EL in accommodating diverse model conditions, and (ii) to facilitate the use of well-established Markov Chain Monte Carlo sampling schemes as a convenient alternative to the complex optimization typi...
• Inference for structural changes in nonstationary functional time series with partial measurement error AI翻译
  作者：Bai, Lujia; Hu, Qirui; Wu, Weichi
  作者单位：Ruhr University Bochum; Shanghai University of Finance & Economics; Shanghai University of Finance & Economics; Tsinghua University
  摘要：We study the problem of detecting and localizing change points for a general class of locally stationary functional time series. To accommodate the nonstationarity and other possible complex features, such as discontinuous trajectories, and heterogeneous partial measurement error of contemporary functional data, we propose methods that do not rest on the preprocessing techniques of presmoothing and dimension-reduction, which would be less accurate without the assumptions of stationarity and co...
• Professor Garib Nath Singh's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Nick Whiteley et al. AI翻译
  作者：Singh, Garib Nath
  作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (Indian School of Mines) Dhanbad
• Unbiased and consistent nested sampling via sequential Monte Carlo AI翻译
  作者：Salomone, Robert; South, Leah F.; Drovandi, Christopher; Kroese, Dirk P.; Johansen, Adam M.
  作者单位：Queensland University of Technology (QUT); Queensland University of Technology (QUT); University of Queensland; University of Warwick
  摘要：We introduce a new class of sequential Monte Carlo methods which reformulates the essence of the nested sampling (NS) method of Skilling in terms of sequential Monte Carlo techniques. Two new algorithms are proposed: nested sampling via sequential Monte Carlo (NS-SMC) and adaptive nested sampling via sequential Monte Carlo (ANS-SMC). The new framework allows convergence results to be obtained in the setting when Markov chain Monte Carlo (MCMC) is used to produce new samples. An additional bene...