Home > Department of Statistics > Events > 2014-15 Seminar Series > Statistics Seminar Series 2014-15


Department of Statistics

Columbia House

London School of Economics

Houghton Street




General enquiries about events and seminars in the Department of Statistics

Email: statistics.events@lse.ac.uk|


Enquiries about undergraduate and postgraduate course programmes in the Department of Statistics


Online query form|


Frequently asked questions|


BSc Queries

+44 (0)20 7955 7650


MSc Queries

+44 (0)20 7955 6879 


MPhil/PhD Queries

+44 (0)20 7955 751


Statistics Seminar Series 2014-15

The Department of Statistics hosts statistics seminars throughout the year. Seminars take place on Friday afternoons at 2pm, unless otherwise stated, in the Leverhulme Library (COL 6.15, Columbia House). All are very welcome to attend. Please contact Events| for further information about any of these seminars 

Details of the 2014-15 Statistics Seminar Series will be published here as they are confirmed.

GPlobidisFriday 17 October 2014, 2pm - 3pm, Room COL 6.15, Columbia House (sixth floor)
Maps and directions|

George Ploubidis
Institute of Education, University of London|

Title: Psychological distress in mid-life in 1958 and 1970 cohorts: the role of childhood experiences and behavioural adjustment

Abstract:  This paper addresses the levels of psychological distress experienced in mid-life (age 42) by men and women born in 1958 and 1970, using two well known population based UK birth cohorts (NCDS and BCS70). Our aim was to empirically test whether psychological distress has increased, and if so whether this increase can be explained by differences between the cohorts in their childhood conditions (including birth and parental characteristics), as well as differences in their social and emotional adjustment during adolescence. The measurement equivalence of psychological distress between the two cohorts was formally established using methods within the generalised latent variable modelling framework. The potential role of childhood conditions, social and behavioural adjustment in explaining between cohort differences was investigated with modern causal mediation methods. Differences with respect to psychological distress between the NCDS and BCS70 cohorts at age 42 were observed, with the BCS70 being on average more psychologically distressed. These differences were more pronounced in men, with the magnitude of the effect being twice as strong compared to women. For both men and women it appears this effect is not due to the hypothesised factors in early life and adolescence, since these accounted for only 15% of the between cohort difference in men and 20% in women.

LTruquetFriday 31 October 2014, 2pm - 3pm, Room COL 6.15, Columbia House (sixth floor)
Maps and directions|

Lionel Truquet
Université de Rennes|

Title: Statistical inference in semiparametric locally stationary ARCH models

Abstract:  In this work, we consider semiparametric versions of the univariate time-varying ARCH(p) model introduced by Dahlhaus & Subba Rao (2006) and studied by Fryzlewicz, Sapatinas and Subba Rao (2008). For a  given nonstationary data set, a natural question is to determine which coefficients capture the nonstationarity  and then which coefficients can be assumed to be non time-varying. For example, when the intercept is the  single time-varying coefficient, the resulting model is close to a multiplicative volatility model in the sense  of Engle & Rangel (2008) or Hafner and Linton (2010). Using kernel estimation, we will first explain how  to estimate the parametric and the nonparametric component of the volatility and how to obtain an asymptotically  efficient estimator of the parametric part when the noise is Gaussian. The problem of testing whether  some coefficients are constant or not is also addressed. In particular, our procedure can be used to test the  existence of a second-order dynamic in this nonstationary framework. Our methodology can be adapted to  more general linear regression models with time-varying coefficients, in the spirit of Zhang & Wu (2012).

[1] Dahlhaus, R., Rao, S.S. Statistical inference for time-varying ARCH processes. The Annals of Statistics, 2006, Vol. 34, No. 3, 1075 - 1114.
[2] Engle, R. F., Rangel, J. G. The spline-GARCH model for low-frequency volatility and its global macroeconomic causes. Rev. Financ. Stud. (2008) 21 (3).
[3] Fryzlewicz, P., Sapatinas, T., Subba Rao S. Normalized least-squares estimation in time-varying ARCH models. The Annals of Statistics (2008), Vol. 36, No. 2, 742-786.
[4] Hafner, C. M., Linton, O. Efficient estimation of a multivariate multiplicative volatility model. Journal of Econometrics (2010), Vol. 159, Issue 1, 55-73.
[5] Zhang, T., Wu, W.B. Inference of time-varying regression models. The Annals of Statistics (2012), Vol.40, No. 3, 1376-1402.

PNultyFriday 14 November 2014, 2pm - 3pm, Room COL 6.15, Columbia House (sixth floor)
Maps and directions|

Paul Nulty
LSE (Department of Methodology)|

Title: Tools and Methods for Quantitative Text Analysis

Abstract: In this talk I present an overview of methods used for quantitative analysis of large text corpora. I begin by describing practical issues involved in using software to retrieve information from large text files, online text, and social media text streams. I discuss how text is transformed for quantitative analysis by extracting a word frequency matrix or other relevant features for machine learning, and describe software in development on the QUANTESS project to facilitate this process. Finally, I will discuss the statistical properties of natural language text, and present ongoing research on improving methods for extracting features from text for use with standard machine learning algorithms, with application to the scaling of political texts

Please also see the Big Data Initiative Seminar Series| page

YFengFriday 28 November 2014, 2pm - 3pm, Room COL 6.15, Columbia House (sixth floor)
Maps and directions|

Yang Feng
Columbia University|


Title: Model Selection in High-Dimensional Misspecified Models

Abstract: Model selection is indispensable to high-dimensional sparse modeling in selecting the best set of covariates among a sequence of candidate models. Most existing work assumes implicitly that the model is correctly specified or of fixed dimensions. Yet model misspecification and high dimensionality are common in real applications. In this paper, we investigate two classical Kullback-Leibler divergence and Bayesian principles of model selection in the setting of high-dimensional misspecified models. Asymptotic expansions of these principles reveal that the effect of model misspecification is crucial and should be taken into account, leading to the generalized AIC and generalized BIC in high dimensions. With a natural choice of prior probabilities, we suggest the generalized BIC with prior probability which involves a logarithmic factor of the dimensionality in penalizing model complexity. We further establish the consistency of the covariance contrast matrix estimator in a general setting. Our results and new method are supported by numerical studies.


LPMAFriday 12 December 2014, 2pm - 3pm, Room COL 6.15, Columbia House (sixth floor)
Maps and directions|

Ismaël Castillo
Laboratoire de Probabilités et Modèles Aléatoires, Universities Paris VI and VII|

Title: to be confirmed

Abstract: to be confirmed

DJHandFriday 20 February 2015 2pm - 3pm Room CLM 3.02 Clement House (third floor)
Map and directions|

David Hand
Imperial College London|

Title: to be confirmed

Abstract: to be confirmed

SofiaOlhedeFriday 20 March 2015, 2pm - 3pm, Room COL 6.15, Columbia House (sixth floor)
Maps and directions|

Sofia Olhede
University College London|

Title: to be confirmed

Abstract: to be confirmed