Skip to main content Skip to navigation

CRiSM Seminars

Seminars take place in Room A1.01, Dept of Statistics, University of Warwick at 4pm, unless otherwise stated. There will be tea, coffee and biscuits in the Statistics Common Room (Room C0.06) at 3.30pm. After the seminar there will usually be wine and snacks.

In particular, we ask all postgraduate students to attend the seminars. Please come and join us for a glass of wine afterwards.

For more information on the CRiSM seminar series please contact Dr Julia Brettschneider, email Julia dot Brettschneider at warwick dot ac dot uk.


Tue 25 Jun, '19
CRiSM Seminar

Prof. Malgorzata Bogdan, University of Wroclaw, Poland (15:00-16:00)

Abstract: Sorted L-One Penalized Estimator is a relatively new convex optimization procedure for identifying predictors in large data bases.
In this lecture we will present the method, some theoretical and empirical results illustrating its properties and the applications in the context of genomic and medical data. Apart from the classical version of SLOPE we will also discuss its spike and slab version, aimed at reducing the bias of estimators of regression coefficients. When discussing SLOPE we will also present some new theoretical results on the probability of discovering the true model by LASSO (which is a specific instance of
SLOPE) and its thresholded version.

Fri 28 Jun, '19
CRiSM Seminar

Dr. Pauline O'Shaughnessy, University of Wollongong, Australia

Title: Bootstrap inference in the longitudinal data with multiple sources of variation

Abstract: Linear mixed models allow us to model the dependence among the responses by incorporating random effects. Such dependence inherent in the longitudinal data from a complex design can be from the clustering between subjects and the repeated measurements within the subject. When the underlying distribution is not fully specified, we consider a class of estimators defined by the Gaussian quasi-likelihood for normal-like response variable. Historically it is challenging to make inference about the variance components in the framework of mixed models. We propose a new weighted estimating equation bootstrap, which varies weight schemes for different parameter estimators. The performance of the weighted estimating equation bootstrap is empirically evaluated in the simulation studies, showing improved coverage and variance estimation for the variance component estimators under models with normal and non-normal distributions for random effects. The asymptotic properties will also be addressed and we apply this new bootstrap method to a longitudinal dataset in biology.

(This is a joint work with Professor Alan Welsh from the Australian National University.)

Previous seminars:

All CRiSM seminars since July 2007

2007 Feb-June

2006 Sep-Dec

2006 Jan-May

2005 Sep-Dec


Search for: