Statistics Seminar Series


Seminars take place in the Leverhulme Library (6.15), located on the sixth floor of Columbia House.

Statistics is all about getting data and analysing it and using it to answer questions about the world be that in terms of Economics, Finance or public opinions. The applications are numerous

The Department of Statistics hosts Statistics seminars throughout the year. Seminars usually take place on Friday afternoons at 2pm in the Leverhulme Library (unless stated otherwise) with refreshments preceding at 1.30pm.

All are very welcome to attend! Please contact Shanice Kudita at for further information about any of these seminars below.

Seminars in Michealmas Term 2019 

Friday 22nd November, 2pm - Paolo Zaffaroni, Imperial College London


Factor Models for Conditional Asset Pricing


This paper develops a methodology for inference  on  conditional asset pricing  models linear in latent risk factors, valid when the number of  assets  diverges but the time series dimension is fixed, possibly very small. We show that the no-arbitrage condition permits to identify the risk premia as the expectation  of   the  latent  risk factors. This result paves the way to an inferential procedure for the factors' risk premia and for the stochastic discount factor,  spanned by the latent  risk factors. In our set up every feature of the asset pricing model  is allowed to be time-varying including loadings,  risk premia, idiosyncratic risk and the number of risk factors   Monte Carlo experiments corroborate our theoretical findings.

Several empirical applications based on individual asset returns data demonstrate the power of the methodology, allowing to tease out the empirical content of the time-variation stemming  from asset pricing theory.

Please find more information on the speaker here:

Friday 6th December, 2pm - Yoav Zemel, University of Cambridge


The Procrustes Metric on Covariance Operators is Optimal Transport:

Statistical Implications


Covariance operators are fundamental in functional data analysis, providing the canonical means to analyse functional variation via the celebrated Karhunen-Loève expansion. These operators may themselves be subject to variation, for instance in contexts where multiple functional populations are to be compared. Statistical techniques to analyse such variation are intimately linked with the choice of metric on covariance operators, and the intrinsic infinite-dimensionality of these operators.

  We identify the Procrustes metric on trace-class infinite-dimensional covariance operators with the Wasserstein distance of optimal transport.

  The identification allows us to provide a detailed description of aspects of this distance that are of statistical importance, including:

the manifold-like geometry of the space of covariance operators endowed with the Procrustes distance; key properties of the Fréchet mean of a random sample of covariance; and a data-generating mechanism associated with this distance.  We take advantage of these properties for carrying out principal component analysis on covariance operators and for testing homogeneity of covariances of several functional populations using the optimal transport maps.

(joint work with Valentina Masarotto and Victor M. Panaretos)

Please find more information about the speaker here:

Friday 13th December, 2pm - Jinchi Lv, University of Southern California


Asymptotic Distributions of High-Dimensional Nonparametric Inference with Distance Correlation


Asymptotic Distributions of High-Dimensional Nonparametric Inference with Distance Correlation" Abstract:Understanding the nonlinear association between a pair of potentially high-dimensional random vectors is encountered frequently in many contemporary big data applications. Distance correlation has become an increasingly popular tool for such a purpose. Most existing works have explored its asymptotic distributions under the independence assumption when only the sample size or the dimensionality diverges. Yet its asymptotic theory for the more realistic setting when both sample size and dimensionality diverge remains largely unexplored. In this paper, we fill such a gap and establish the central limit theorems and the associated rates of convergence for a rescaled test statistic based on the bias-corrected distance correlation in high dimensions under some mild regularity conditions and the null hypothesis of independence between the two random vectors. Our new theoretical results reveal an interesting phenomenon of blessing of dimensionality for high-dimensional nonparametric inference with distance correlation in the sense that the accuracy of normal approximation can increase with dimensionality. The finite-sample performance and advantages of the test statistic are illustrated with several simulation examples and a blockchain application. This is a joint work with Lan Gao and Qiman Shao.
Short bio:Jinchi Lv is Kenneth King Stonier Chair in Business Administration and Professor in Data Sciences and Operations Department of the Marshall School of Business at the University of Southern California, Professor in Department of Mathematics at USC, and an Associate Fellow of USC Dornsife Institute for New Economic Thinking (INET). He received his Ph.D. in Mathematics from Princeton University in 2007. He was McAlister Associate Professor in Business Administration at USC from 2016-2019. His research interests include statistics, machine learning, data science, business applications, and artificial intelligence and blockchain.
His papers have been published in journals in statistics, economics, computer science, information theory, and biology, and one of them was published as a Discussion Paper in Journal of the Royal Statistical Society Series B (2008). He is the recipient of Fellow of Institute of Mathematical Statistics (2019), USC Marshall Dean's Award for Research Impact (2017), Adobe Data Science Research Award (2017), the Royal Statistical Society Guy Medal in Bronze (2015), NSF Faculty Early Career Development (CAREER) Award (2010), USC Marshall Dean's Award for Research Excellence (2009), and Zumberge Individual Award from USC's James H. Zumberge Faculty Research and Innovation Fund (2008). He has served as an associate editor of the Annals of Statistics (2013-2018), Journal of Business & Economic Statistics (2018-present), and Statistica Sinica (2008-2016)

Please find more information about the speaker here:


Seminars in Lent Term 2020 

Friday 31st January, 2pm - Piotr Zwiernik, University Pompeu Fabra, Barcelona


Totally positive distributions: graphical models, and convex optimization


Probability distributions that are multivariate totally positive of order 2 (MTP2) appeared in the theory of positive dependence and in statistical physics through the celebrated FKG inequality. The MTP2 property is stable under marginalization, conditioning and it appears naturally in various probabilistic graphical models with hidden variables. Models of exponential families with the MTP2 property admit a unique maximum likelihood estimator. In the Gaussian case, the MLE exists also in high-dimensional settings, when p>n, and it leads to sparse solutions. The main aim of this lecture is to give an idea of what the MTP2 condition is as well as to show how total positivity becomes useful in statistical modelling. I will discuss in more detail two cases: the Gaussian distribution and the binary Ising model. 

Please see more information here:

Friday 14th February, 2pm - Dr Konstantinos Fokianos, University of Lancaster


Abstract: The concept of distance covariance/correlation was introduced recently to characterize dependence

among vectors of random variables. We review some statistical aspects of distance covariance/

correlation function, and we demonstrate its applicability to time series analysis. We will see

that the auto-distance covariance/correlation function is able to identify non-linear relationships,

 can be employed for testing the i.i.d. hypothesis and for non-parametric change-point detection.

Friday 28th February, 2pm - Dr Judith Rousseau, University of Oxford

Estimatig The Interraction functions and the graph of interactions in Multivariate Hawkes processes using Bayesian Non parametric Methods

More information is avaiable here  


Friday 27th March, 2pm - Dr Sarah Filippi, Imperial College London

More details will be confirmed nearer the date.

Please see more information here:


Seminars for Summer Term

Wednesday 6th May, 2pm - Dr Xin Tong, USC Marshall

 More details will be confirmed nearer the date.

Please see more information here:


Wednesday 13th May, 2pm, Dr Yang Feng, NYU

More details will be confirmed nearer the date.

Please see more information here: