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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: to be confirmed

Abstract: to be confirmed

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: to be confirmed

Abstract: to be confirmed

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

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