Browsing by Author "Samworth, Richard John"

Now showing items 1-11 of 11

  • Comments on: High-dimensional simultaneous inference with the bootstrap 

    Lockhart, RA; Samworth, Richard John (Sociedad de Estadistica e Investigacion OperativaTest, 2017-12-01)
    We congratulate the authors on their stimulating contribution to the burgeoning high-dimensional inference literature. The bootstrap offers such an attractive methodology in these settings, but it is well-known that its ...

  • Discussion of ‘An adaptive resampling test for detecting the presence of significant predictors’ by I. W. McKeague and M. Qian 

    Shah, Rajen D; Samworth, Richard John (Taylor & FrancisJournal of the American Statistical Association, 2016-01-15)
    We are grateful for the opportunity to discuss this new test, based on marginal screening, of a global null hypothesis in linear models. Marginal screening has become a very popular tool for reducing dimensionality in ...

  • Generalised additive and index models with shape constraints 

    Chen, Yining; Samworth, Richard John (WileyJournal of the Royal Statistical Society: Series B (Statistical Methodology), 2015-10-26)
    We study generalized additive models, with shape restrictions (e.g. monotonicity, convexity and concavity) imposed on each component of the additive prediction function. We show that this framework facilitates a non-parametric ...

  • Global Rates of Convergence in Log-Concave Density Estimation 

    Kim, Arlene KH; Samworth, Richard John (Institute of Mathematical StatisticsAnnals of Statistics, 2016)
    The estimation of a log-concave density on $\Bbb R$$^d$ represents a central problem in the area of nonparametric inference under shape constraints. In this paper, we study the performance of log-concave density estimators ...

  • Handbook of Big Data [Book review] 

    Samworth, Richard John (WileyStatistics in Medicine, 2016-12-01)

  • High-dimensional change point estimation via sparse projection 

    Wang, T; Samworth, Richard John
    Changepoints are a very common feature of Big Data that arrive in the form of a data stream. In this paper, we study high-dimensional time series in which, at certain time points, the mean structure changes in a sparse ...

  • New approaches to modern statistical classification problems 

    Cannings, Timothy Ivor (University of CambridgeDepartment of Pure Mathematics and Mathematical Statistics, 2015-11-10)
    This thesis concerns the development and mathematical analysis of statistical procedures for classification problems. In supervised classification, the practitioner is presented with the task of assigning an object to ...

  • Peter Hall’s Work on High-Dimensional Data and Classification 

    Samworth, Richard John (Institute of Mathematical StatisticsAnnals of Statistics, 2016)
    In this article, I summarise Peter Hall’s contributions to high-dimensional data, including their geometric representations and variable selection methods based on ranking. I also discuss his work on classification problems, ...

  • Random-projection ensemble classification 

    Cannings, Timothy Ivor; Samworth, Richard John
    We introduce a very general method for high-dimensional classification, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional ...

  • Statistical and computational trade-offs in estimation of sparse principal components 

    Wang, Tengyao; Berthet, Quentin; Samworth, Richard John (Institute of Mathematical StatisticsAnnals of Statistics, 2016)
    In recent years, Sparse Principal Component Analysis has emerged as an extremely popular dimension reduction technique for highdimensional data. The theoretical challenge, in the simplest case, is to estimate the leading ...

  • Variable selection with error control: Another look at Stability Selection 

    Shah, Rajen Dinesh; Samworth, Richard John (Wiley on behalf of the Royal Statistical SocietyJournal of the Royal Statistical Society: Series B (Statistical Methodology), 2012-06-21)
    Stability Selection was recently introduced by Meinshausen and B¨uhlmann (2010) as a very general technique designed to improve the performance of a variable selection algorithm. It is based on aggregating the results of ...