Journal of the Royal Statistical Society: Series B

最新文章

• The Barker proposal: Combining robustness and efficiency in gradient-based MCMC AI翻译
  作者：Livingstone, Samuel; Zanella, Giacomo
  作者单位：University of London; University College London; Bocconi University; Bocconi University
  摘要：There is a tension between robustness and efficiency when designing Markov chain Monte Carlo (MCMC) sampling algorithms. Here we focus on robustness with respect to tuning parameters, showing that more sophisticated algorithms tend to be more sensitive to the choice of step-size parameter and less robust to heterogeneity of the distribution of interest. We characterise this phenomenon by studying the behaviour of spectral gaps as an increasingly poor step-size is chosen for the algorithm. Moti...
• Peng Ding's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Ding, Peng
  作者单位：University of California System; University of California Berkeley
• Simulation of multivariate diffusion bridges (vol 78, pg 343, 2016) AI翻译
  作者：Bladt, Mogens; Finch, Samuel; Sorensen, Michael
  作者单位：University of Copenhagen
  摘要：We correct an error in Theorem 1 in Bladt et al. (2016) Journal of the Royal Statistical Society: Series B, 78, 343-369 by changing the initial distribution of an auxiliary diffusion process, which is used to describe the distribution of the proposed approximate diffusion bridges. As a consequence, we correct two algorithms for simulating exact diffusion bridges by changing the initial distribution of auxiliary diffusion processes in the same way. Simulation studies affected by the error are r...
• Nonparametric, tuning-free estimation of S-shaped functions AI翻译
  作者：Feng, Oliver Y.; Chen, Yining; Han, Qiyang; Carroll, Raymond J.; Samworth, Richard J.
  作者单位：University of Cambridge; University of London; London School Economics & Political Science; Rutgers University System; Rutgers University New Brunswick; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
  摘要：We consider the nonparametric estimation of an S-shaped regression function. The least squares estimator provides a very natural, tuning-free approach, but results in a non-convex optimization problem, since the inflection point is unknown. We show that the estimator may nevertheless be regarded as a projection onto a finite union of convex cones, which allows us to propose a mixed primal-dual bases algorithm for its efficient, sequential computation. After developing a projection framework th...
• Semiparametric latent class analysis of recurrent event data AI翻译
  作者：Zhao, Wei; Peng, Limin; Hanfelt, John
  作者单位：Emory University; Shandong University
  摘要：Recurrent event data frequently arise in chronic disease studies, providing rich information on disease progression. The concept of latent class offers a sensible perspective to characterize complex population heterogeneity in recurrent event trajectories that may not be adequately captured by a single regression model. However, the development of latent class methods for recurrent event data has been sparse, typically requiring strong parametric assumptions and involving algorithmic issues. I...
• J. Goseling and MNM van Lieshout's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Goseling, J.; van Lieshout, M. N. M.
  作者单位：University of Twente; Centrum Wiskunde & Informatica (CWI)
• 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...
• Chaohua Dong, Jiti Gao and Oliver Linton's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Dong, Chaohua; Gao, Jiti; Linton, Oliver
  作者单位：Zhongnan University of Economics & Law; Monash University; University of Cambridge
• Kuldeep Kumar's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Kumar, Kuldeep
  作者单位：Bond University
• Sebastian Dietz's contribution to the Discussion of 'Gaussian Differential Privacy' by Dong et al AI翻译
  作者：Dietz, Sebastian