Journal of the Royal Statistical Society: Series B

最新文章

• Non-parametric small area estimation using penalized spline regression AI翻译
  作者：Opsomer, J. D.; Claeskens, G.; Ranalli, M. G.; Kauermann, G.; Breidt, F. J.
  作者单位：Colorado State University System; Colorado State University Fort Collins; KU Leuven; University of Perugia; University of Bielefeld
  摘要：The paper proposes a small area estimation approach that combines small area random effects with a smooth, non-parametrically specified trend. By using penalized splines as the representation for the non-parametric trend, it is possible to express the non-parametric small area estimation problem as a mixed effect model regression. The resulting model is readily fitted by using existing model fitting approaches such as restricted maximum likelihood. We present theoretical results on the predict...
• Sure independence screening for ultrahigh dimensional feature space AI翻译
  作者：Fan, Jianqing; Lv, Jinchi
  作者单位：Princeton University; University of Southern California
  摘要：Variable selection plays an important role in high dimensional statistical modelling which nowadays appears in many areas and is key to various scientific discoveries. For problems of large scale or dimensionality p, accuracy of estimation and computational cost are two top concerns. Recently, Candes and Tao have proposed the Dantzig selector using L-1-regularization and showed that it achieves the ideal risk up to a logarithmic factor log(p). Their innovative procedure and remarkable result a...
• Theoretical measures of relative performance of classifiers for high dimensional data with small sample sizes AI翻译
  作者：Hall, Peter; Pittelkow, Yvonne; Ghosh, Malay
  作者单位：University of Melbourne; Australian National University; State University System of Florida; University of Florida
  摘要：We suggest a technique, related to the concept of 'detection boundary' that was developed by Ingster and by Donoho and Jin, for comparing the theoretical performance of classifiers constructed from small training samples of very large vectors. The resulting 'classification boundaries' are obtained for a variety of distance-based methods, including the support vector machine, distance-weighted discrimination and kth-nearest-neighbour classifiers, for thresholded forms of those methods, and for ...
• Partially linear hazard regression with varying coefficients for multivariate survival data AI翻译
  作者：Cai, Jianwen; Fan, Jianqing; Jiang, Jiancheng; Zhou, Haibo
  作者单位：University of North Carolina; University of North Carolina Charlotte; Princeton University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：The paper studies estimation of partially linear hazard regression models with varying coefficients for multivariate survival data. A profile pseudo-partial-likelihood estimation method is proposed. The estimation of the parameters of the linear part is accomplished via maximization of the profile pseudo-partial-likelihood, whereas the varying-coefficient functions are considered as nuisance parameters that are profiled out of the likelihood. It is shown that the estimators of the parameters a...
• The group lasso for logistic regression AI翻译
  作者：Meier, Lukas; van de Geer, Sara A.; Buhlmann, Peter
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
  摘要：The group lasso is an extension of the lasso to do variable selection on (predefined) groups of variables in linear regression models. The estimates have the attractive property of being invariant under groupwise orthogonal reparameterizations. We extend the group lasso to logistic regression models and present an efficient algorithm, that is especially suitable for high dimensional problems, which can also be applied to generalized linear models to solve the corresponding convex optimization ...
• Stationary process approximation for the analysis of large spatial datasets AI翻译
  作者：Banerjee, Sudipto; Gelfand, Alan E.; Finley, Andrew O.; Sang, Huiyan
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Duke University; Michigan State University
  摘要：With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. Over the last decade, hierarchical models implemented through Markov chain Monte Carlo methods have become especially popular for spatial modelling, given their flexibility and power to fit models that would be infeasible with classical methods as well as their avoidance of possibly inappropriate asymptotics. However, fitting hierarchica...
• Binary models for marginal independence AI翻译
  作者：Drton, Mathias; Richardson, Thomas S.
  作者单位：University of Washington; University of Washington Seattle; University of Chicago
  摘要：Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach ...
• Modelling multivariate volatilities via conditionally uncorrelated components AI翻译
  作者：Fan, Jianqing; Wang, Mingjin; Yao, Qiwei
  作者单位：Princeton University; University of London; London School Economics & Political Science; Peking University
  摘要：We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrix-valued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high dimensional optimization problem into several lower dimensional subproblems. Consistency for the estimated CUCs has been establishe...
• Variable selection in semiparametric linear regression with censored data AI翻译
  作者：Johnson, Brent A.
  作者单位：Emory University; Rollins School Public Health
  摘要：We describe two procedures for selecting variables in the semiparametric linear regression model for censored data. One procedure penalizes a vector of estimating equations and simultaneously estimates regression coefficients and selects submodels. A second procedure controls systematically the proportion of unimportant variables through forward selection and the addition of pseudorandom variables. We explore both rank-based statistics and Buckley-James statistics in the setting proposed and e...
• Robust estimation in the normal mixture model based on robust clustering AI翻译
  作者：Cuesta-Albertos, J. A.; Matran, C.; Mayo-Iscar, A.
  作者单位：Universidad de Cantabria; Universidad de Valladolid
  摘要：We introduce a robust estimation procedure that is based on the choice of a representative trimmed subsample through an initial robust clustering procedure, and subsequent improvements based on maximum likelihood. To obtain the initial trimming we resort to the trimmed k-means, a simple procedure designed for finding the core of the clusters under appropriate configurations. By handling the trimmed data as censored, maximum likelihood estimation provides in each step the location and shape of ...