Journal webinar

How does the Journal webinar work?

Journal webinars are held every few months and last about an hour. Journal papers are carefully selected from recent issues of the Royal Statistical Society's journals by editorial board for their importance, relevance and/or use of cutting-edge methodology; and authors are invited to present their work and take questions from an audience who 'dial in' to access the webinar.

Two papers are selected from our journals and authors will be invited to present their papers (20 minutes) followed by discussion (25 minutes) for each paper. Attendees will dial into a teleconference call. Papers and slides will be available to download two weeks in advance or you can log into the conference system and follow the presentation live online. These sessions are open to members and non-members. No need to pre-register. Audio recordings will be available for download shortly after the session.

Questions on the paper or general queries can be emailed in advance of the session to journalwebinar@rss.org.uk.

Dial-in details

For UK and international users the link to the Skype for Business app is provided on this page.

Next event

2 April at 4pm (UK time); 11am (EDT or UTC-4h)

Paper: ‘Confidence intervals for low dimensional parameters in high dimensional linear models’ by Cun-Hui Zhang and Stephanie S Zhang. [Download slides - PDF]

The paper was published in JRSS Series B (Vol 76:1) in January 2014. It is an open access paper available from Wiley Online Library

Abstract: The purpose of this paper is to propose methodologies for statistical inference of low dimensional parameters with high dimensional data. We focus on constructing confidence intervals for individual coefficients and linear combinations of several of them in a linear regression model, although our ideas are applicable in a much broader context. The theoretical results that are presented provide sufficient conditions for the asymptotic normality of the proposed estimators along with a consistent estimator for their finite dimensional covariance matrices. These sufficient conditions allow the number of variables to exceed the sample size and the presence of many small non‐zero coefficients. Our methods and theory apply to interval estimation of a preconceived regression coefficient or contrast as well as simultaneous interval estimation of many regression coefficients. Moreover, the method proposed turns the regression data into an approximate Gaussian sequence of point estimators of individual regression coefficients, which can be used to select variables after proper thresholding. The simulation results that are presented demonstrate the accuracy of the coverage probability of the confidence intervals proposed as well as other desirable properties, strongly supporting the theoretical results.

Presenter: Cun-Hui Zhang will present and discuss his paper ‘Confidence intervals for low dimensional parameters in high dimensional linear models’. The paper is co-authored by Stephani S Zhang.
Chair: Yi Yu, University of Bristol
Discussants: Andrea Montenari, Stanford University and Sara van de Geer, ETH Zurich

An open discussion led by our discussants will follow the presentation by Cun-Hui Zhang in which everyone is encouraged to take part. You can ask the author a question over the phone or type a message if you prefer using the web based teleconference system (Skype). Questions can also be emailed in advance and further information requested from journalwebinar@rss.org.uk

Journal webinars are free, open to everyone and simple to join.

Those unable to listen in live can listen to the podcast and view slides from the presentation afterwards on YouTube

.

More information

Past events

Webcasts, MP3s and slides from past events are available to download.