Journal of the Royal Statistical Society: Series B

最新文章

• On selection of spatial linear models for lattice data AI翻译
  作者：Zhu, Jun; Huang, Hsin-Cheng; Reyes, Perla E.
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of Wisconsin System; University of Wisconsin Madison; Academia Sinica - Taiwan; National Yang Ming Chiao Tung University
  摘要：Spatial linear models are popular for the analysis of data on a spatial lattice, but statistical techniques for selection of covariates and a neighbourhood structure are limited. Here we develop new methodology for simultaneous model selection and parameter estimation via penalized maximum likelihood under a spatial adaptive lasso. A computationally efficient algorithm is devised for obtaining approximate penalized maximum likelihood estimates. Asymptotic properties of penalized maximum likeli...
• Ordering and selecting components in multivariate or functional data linear prediction AI翻译
  作者：Hall, Peter; Yang, You-Jun
  作者单位：University of Melbourne; National Taiwan University
  摘要：The problem of component choice in regression-based prediction has a long history. The main cases where important choices must be made are functional data analysis, and problems in which the explanatory variables are relatively high dimensional vectors. Indeed, principal component analysis has become the basis for methods for functional linear regression. In this context the number of components can also be interpreted as a smoothing parameter, and so the viewpoint is a little different from t...
• Stability selection AI翻译
  作者：Meinshausen, Nicolai; Buehlmann, Peter
  作者单位：University of Oxford; Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：Estimation of structure, such as in variable selection, graphical modelling or cluster analysis, is notoriously difficult, especially for high dimensional data. We introduce stability selection. It is based on subsampling in combination with (high dimensional) selection algorithms. As such, the method is extremely general and has a very wide range of applicability. Stability selection provides finite sample control for some error rates of false discoveries and hence a transparent principle to ...
• A multiresolution approach to time warping achieved by a Bayesian prior-posterior transfer fitting strategy AI翻译
  作者：Claeskens, Gerda; Silverman, Bernard W.; Slaets, Leen
  作者单位：KU Leuven; KU Leuven; University of Oxford
  摘要：Warping is an approach to the reduction and analysis of phase variability in functional observations, by applying a smooth bijection to the function argument. We propose a natural representation of warping functions in terms of a new type of elementary functions named 'warping component functions', or 'warplets', which are combined into the warping function by composition. The inverse warping function is trivial and explicit to obtain. A sequential Bayesian estimation strategy is introduced wh...
• Minimally informative prior distributions for non-parametric Bayesian analysis AI翻译
  作者：Bush, Christopher A.; Lee, Juhee; MacEachern, Steven N.
  作者单位：University System of Ohio; Ohio State University; Novartis; Novartis USA
  摘要：We address the problem of how to conduct a minimally informative, non-parametric Bayesian analysis. The central question is how to devise a model so that the posterior distribution satisfies a few basic properties. The concept of 'local mass' provides the key to the development of the limiting Dirichlet process model. This model is then used to provide an engine for inference in the compound decision problem and for multiple-comparisons inference in a one-way analysis-of-variance setting. Our ...
• Sufficient dimension reduction for spatial point processes directed by Gaussian random fields AI翻译
  作者：Guan, Yongtao; Wang, Hansheng
  作者单位：Yale University
  摘要：We develop a sufficient dimension reduction paradigm for inhomogeneous spatial point processes driven by Gaussian random fields. Specifically, we introduce the notion of the kth-order central intensity subspace. We show that a central subspace can be defined as the combination of all central intensity subspaces. For many commonly used spatial point process models, we find that the central subspace is equivalent to the first-order central intensity subspace. To estimate the latter, we propose a...
• Semiparametric marginal regression analysis for dependent competing risks under an assumed copula AI翻译
  作者：Chen, Yi-Hau
  作者单位：Academia Sinica - Taiwan
  摘要：Competing risks problems arise in many fields of science, where two or more types of event may occur on a subject, but only the event occurring first is observed together with its occurrence time, and other events are censored. The marginal and joint distributions of event times for competing risks cannot be identified from the observed data without assuming the relationship between events. The commonly adopted independent censoring assumption may be easily violated. An alternative is to assum...
• Local additive estimation AI翻译
  作者：Park, Juhyun; Seifert, Burkhardt
  作者单位：Lancaster University; University of Zurich
  摘要：Additive models are popular in high dimensional regression problems owing to their flexibility in model building and optimality in additive function estimation. Moreover, they do not suffer from the so-called curse of dimensionality generally arising in non-parametric regression settings. Less known is the model bias that is incurred from the restriction to the additive class of models. We introduce a new class of estimators that reduces additive model bias, yet preserves some stability of the...
• Criteria for surrogate end points based on causal distributions AI翻译
  作者：Ju, Chuan; Geng, Zhi
  作者单位：Peking University
  摘要：When a treatment has a positive average causal effect (ACE) on an intermediate variable or surrogate end point which in turn has a positive ACE on a true end point, the treatment may have a negative ACE on the true end point due to the presence of unobserved confounders, which is called the surrogate paradox. A criterion for surrogate end points based on ACEs has recently been proposed to avoid the surrogate paradox. For a continuous or ordinal discrete end point, the distributional causal eff...
• Local composite quantile regression smoothing: an efficient and safe alternative to local polynomial regression AI翻译
  作者：Kai, Bo; Li, Runze; Zou, Hui
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Local polynomial regression is a useful non-parametric regression tool to explore fine data structures and has been widely used in practice. We propose a new non-parametric regression technique called local composite quantile regression smoothing to improve local polynomial regression further. Sampling properties of the estimation procedure proposed are studied. We derive the asymptotic bias, variance and normality of the estimate proposed. The asymptotic relative efficiency of the estimate wi...