Search
Now showing items 1-7 of 7
Discussion of ‘An adaptive resampling test for detecting the presence of significant predictors’ by I. W. McKeague and M. Qian
(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 ...
Comments on: High-dimensional simultaneous inference with the bootstrap
(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 ...
Statistical and computational trade-offs in estimation of sparse principal components
(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 ...
Generalised additive and index models with shape constraints
(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
(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 ...
Peter Hall’s Work on High-Dimensional Data and Classification
(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, ...
Handbook of Big Data [Book review]
(WileyStatistics in Medicine, 2016-12-01)