Journal of the Royal Statistical Society: Series B

最新文章

• Sparse additive models AI翻译
  作者：Ravikumar, Pradeep; Lafferty, John; Liu, Han; Wasserman, Larry
  作者单位：Carnegie Mellon University; University of California System; University of California Berkeley
  摘要：We present a new class of methods for high dimensional non-parametric regression and classification called sparse additive models. Our methods combine ideas from sparse linear modelling and additive non-parametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. Sparse additive models are essentially a functional version of the grouped lasso of Yuan and Lin. They are also closely related...
• Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data AI翻译
  作者：Rizopoulos, Dimitris; Verbeke, Geert; Lesaffre, Emmanuel
  作者单位：Erasmus University Rotterdam; Erasmus MC; KU Leuven; Hasselt University
  摘要：A common objective in longitudinal studies is the joint modelling of a longitudinal response with a time-to-event outcome. Random effects are typically used in the joint modelling framework to explain the interrelationships between these two processes. However, estimation in the presence of random effects involves intractable integrals requiring numerical integration. We propose a new computational approach for fitting such models that is based on the Laplace method for integrals that makes th...
• DASSO: connections between the Dantzig selector and lasso AI翻译
  作者：James, Gareth M.; Radchenko, Peter; Lv, Jinchi
  作者单位：University of Southern California
  摘要：We propose a new algorithm, DASSO, for fitting the entire coefficient path of the Dantzig selector with a similar computational cost to the least angle regression algorithm that is used to compute the lasso. DASSO efficiently constructs a piecewise linear path through a sequential simplex-like algorithm, which is remarkably similar to the least angle regression algorithm. Comparison of the two algorithms sheds new light on the question of how the lasso and Dantzig selector are related. In addi...
• Smoothing parameter selection for a class of semiparametric linear models AI翻译
  作者：Reiss, Philip T.; Ogden, R. Todd
  作者单位：New York University; Nathan Kline Institute for Psychiatric Research; Columbia University
  摘要：Spline-based approaches to non-parametric and semiparametric regression, as well as to regression of scalar outcomes on functional predictors, entail choosing a parameter controlling the extent to which roughness of the fitted function is penalized. We demonstrate that the equations determining two popular methods for smoothing parameter selection, generalized cross-validation and restricted maximum likelihood, share a similar form that allows us to prove several results which are common to bo...
• Finding an unknown number of multivariate outliers AI翻译
  作者：Riani, Marco; Atkinson, Anthony C.; Cerioli, Andrea
  作者单位：University of London; London School Economics & Political Science; University of Parma
  摘要：We use the forward search to provide robust Mahalanobis distances to detect the presence of outliers in a sample of multivariate normal data. Theoretical results on order statistics and on estimation in truncated samples provide the distribution of our test statistic. We also introduce several new robust distances with associated distributional results. Comparisons of our procedure with tests using other robust Mahalanobis distances show the good size and high power of our procedure. We also p...
• Consistent model selection and data-driven smooth tests for longitudinal data in the estimating equations approach AI翻译
  作者：Wang, Lan; Qu, Annie
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Model selection for marginal regression analysis of longitudinal data is challenging owing to the presence of correlation and the difficulty of specifying the full likelihood, particularly for correlated categorical data. The paper introduces a novel Bayesian information criterion type model selection procedure based on the quadratic inference function, which does not require the full likelihood or quasi-likelihood. With probability approaching 1, the criterion selects the most parsimonious co...
• Variance estimation in the analysis of microarray data AI翻译
  作者：Wang, Yuedong; Ma, Yanyuan; Carroll, Raymond J.
  作者单位：University of California System; University of California Santa Barbara; Texas A&M University System; Texas A&M University College Station; University of Neuchatel
  摘要：Microarrays are one of the most widely used high throughput technologies. One of the main problems in the area is that conventional estimates of the variances that are required in the t-statistic and other statistics are unreliable owing to the small number of replications. Various methods have been proposed in the literature to overcome this lack of degrees of freedom problem. In this context, it is commonly observed that the variance increases proportionally with the intensity level, which h...
• Detecting changes in the mean of functional observations AI翻译
  作者：Berkes, Istvan; Gabrys, Robertas; Horvath, Lajos; Kokoszka, Piotr
  作者单位：Utah System of Higher Education; Utah State University; Utah System of Higher Education; University of Utah; Graz University of Technology
  摘要：Principal component analysis has become a fundamental tool of functional data analysis. It represents the functional data as X-i(t)=mu(t)+Sigma(1 < l
• Large-scale multiple testing under dependence AI翻译
  作者：Sun, Wenguang; Cai, T. Tony
  作者单位：University of Pennsylvania
  摘要：The paper considers the problem of multiple testing under dependence in a compound decision theoretic framework. The observed data are assumed to be generated from an underlying two-state hidden Markov model. We propose oracle and asymptotically optimal data-driven procedures that aim to minimize the false non-discovery rate FNR subject to a constraint on the false discovery rate FDR. It is shown that the performance of a multiple-testing procedure can be substantially improved by adaptively e...
• Hybrid Dirichlet mixture models for functional data AI翻译
  作者：Petrone, Sonia; Guindani, Michele; Gelfand, Alan E.
  作者单位：Bocconi University; University of New Mexico; Duke University
  摘要：In functional data analysis, curves or surfaces are observed, up to measurement error, at a finite set of locations, for, say, a sample of n individuals. Often, the curves are homogeneous, except perhaps for individual-specific regions that provide heterogeneous behaviour (e.g. 'damaged' areas of irregular shape on an otherwise smooth surface). Motivated by applications with functional data of this nature, we propose a Bayesian mixture model, with the aim of dimension reduction, by representin...