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Department of Statistics
Columbia House
London School of Economics
Houghton Street
London
WC2A 2AE

 

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Research in Social Statistics

The department has a long tradition in the area of Social Statistics. This tradition is continued by Professor Chris Skinner, Chair in Statistics, Dr Irini Moustaki, Reader, Dr Wicher Bergsma, Senior Lecturer, Dr Jouni Kuha, Senior Lecturer, Dr Konstantinos Kalogeropoulos, Lecturer, Dr Sara Geneletti, Lecturer, and Dr Matteo Barigozzi, Lecturer.

Our main research areas are latent variable modeling, multilevel and panel modeling, social measurement, statistical model selection, models with measurement error, missing data, marginal modelling, categorical data analysis, causal inference and epidemiology. 


Academic staff in Social Statistics 

barigozzi001Dr Matteo Barigozzi - Lecturer
Research interests: Time series analysis; dynamic factor models (stationary and non-stationary); volatility modelling; graphical models and social networks.
Staff page|

bergsma001Dr Wicher Bergsma – Senior Lecturer
Research interests: Categorical data analysis; multivariate analysis; graphical modelling; measures of association; nonparametric regression; maximum likelihood estimation.
Staff page|

Dr Bergsma is the co-author with Marcel Croon and Jacques A Hagenaars of Marginal models for dependent, clustered and longitudinal categorical data, (2009), published by Springer, New York, USA. ISBN 9780387096094

geneletti001Dr Sara Geneletti – Lecturer
Research interests: Causal inference; graphical models; Bayesian inference; evidence synthesis.
Staff page|

kalogeropoulos001Dr Konstantinos Kalogeropoulos – Lecturer
Research interests: Bayesian inference; Markov chain Monte Carlo; sequential Monte Carlo and inference on models with stochastic differential equations; infectious disease modelling and evidence synthesis.
Staff page|

kuha001Dr Jouni Kuha – Senior Lecturer
Research interests: Model selection; models with measurement error and missing data; categorical data analysis.
Staff page|

moustaki001Dr Irini Moustaki – Reader and Head of Department
Research interests: Latent variable models; categorical data; missing values; applications of latent variable models to education, psychology and to social sciences in general.
Staff page|

Dr Moustaki is the co-author with David Bartholomew and Martin Knott of Latent Variable Models and Factor Analysis: A Unified Approach, to be published by Wiley in June 2011.

skinner001-Professor Chris Skinner - Professor in Statistics
Research interests: Statistical methodology of sample surveys, official statistics and the social sciences, including measurement error, non-responsive and statistical disclosure control.
Staff page|

Professor Skinner is the co-editor with R. L. Chambers of Analysis of survey data, (2003), published by Wiley-VCH, ISBN 9780471899877

A full list of academic and support staff and research students in the Department of Statistics is available here|


Research staff and fellows

koukounariartemis2Dr Artemis Koukounari is a Medical Research Council Population Health Scientist Fellow in the School of Public Health, Imperial College London. She is also an LSE Research Fellow and is currently based in the department while she collaborates on her research with Dr Irini Moustaki.
Imperial College staff page|


Recent research papers

A small selection of our recent research papers:

Alessi, L., Barigozzi, M., and Capasso, M., (2010). Improved penalization for determining the number of factors in approximate factor models|.Statistics and Probability Letters, 80 (23-24), pp. 1806-1813.

Rudas, T., Bergsma, W. P., and Nemeth, R., (2010). Marginal log-linear parameterization of conditional independence models|. Biometrika, 97 (4), pp. 1006-1012.

Geneletti, S., Best, N., and Mason, A., (2010). Adjusting for selection effects in epidemiological studies: why sensitivity analysis is the only "solution"|. Biostatistics, 10 (1), pp. 17-31.

Kalogeropoulos, K., Dellaportas, P., and Roberts, G. O., (2011). Likelihood based inference for correlated diffusions|. Canadian Journal of Statistics, 39 (1), pp. 52-72

Kuha, J., and Goldthorpe, J. H. (2010). Path analysis for discrete variables: The role of education in social mobility|. Journal of the Royal Statistical Society, Series A, 173 (2), pp. 351-369.

Vasdekis, V., Cagnone, S., and Moustaki, I., (2012). Composite likelihood estimation for latent variable models with longitudinal ordinal variables|. Psychometrika, DOI:10.1007/s11336-012-9264-6.

Skinner, C. J., and D'Arrigo, J., (2011). Inverse probability weighting for clustered nonresponse|. Biometrika, 98 (4), pp. 953-966.

Please see the staff pages or LSE Research Online| for a comprehensive list of research outputs.


Research grants

Please see here| for a full list of research grants held by the Department of Statistics and the Centre for the Analysis of Time Series (CATS).


MPhil and PhD Students

Dayan Yehuda 1Dayan, Yehuda
Research topic/title: Some approaches to statistical inference of finite populations
Supervisor(s): Dr Jouni Kuha / Dr Wicher Bergsma

 

Dureau JosephDureau, Joseph
Research topic/title: Non-stationary parameters estimation for non-linear epidemic models
Supervisor(s): Dr Konstantinos Kalogeropoulos / Dr Wicher Bergsma

 

MaiHafezHafez, Mai
Research topic/title: Multivariate longitudinal data with latent variables: missing values and dropout
Supervisor(s): Dr Irini Moustaki / Dr Jouni Kuha

 

A full list of MPhil and PhD students in the Department of Statistics is available  here|.

Recent completions:

Abdey, James (2010)
Thesis title: To p, or not to p? Quantifying inferential decision errors to assess whether significancetruly is significant|

Haider, Sadia (2010)
Thesis title: The dynamics of child poverty in Britain: trends, transistion and trajectories: an analysis of the BHPS (1991-2002)|

Youssef, Noha (2011)
Thesis title: Optimal design for computer experiments as a way to reduce the uncertainty in model prediction  

 

For more informamtion about the MPhil/PhD Statistics programme please see our MPhil/PhD in Statistics| page and MPhil/PhD FAQs|.


A brief introduction to Research in Social Statistics

Please see here| for a brief introduction to research in social statistics.


Conference on social statistics honouring the scientific contributions of Professor Emeritus David J Bartholomew.

Conference LogoThe Department of Statistics hosted a special two-day conference in honour of Professor Emeritus David J Bartholomew on 12 and 13 December 2011, with contributions from several noted speakers.

Full details can be viewed on the conference website|.  


Miscellaneous

Dr Irini Moustaki (far left) gave a talk on Composite likelihood estimation in models with latent variables and random effects on 24 April 2012 at the Composite Likelihood Methods (12w5046)| conference at the Banff International Research Centre for Mathematical Innovation and Discovery.

MoustakiCompositeLikelihoodMethods

 


 

The Department of Statistics was responsible for the construction of the Higher Education Pay and Prices Index published by Universities UK.

 

Latent variable models and factor analysis: a unified approach

LatenetVariableModels

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modelling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques for investigating dependency. 

Marginal models for dependent, clustered and longitudinal categorical data
MarginalModels

Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modelling and offers a wide range of possible applications. Marginal models are often the best choice for answering important research questions when dependent observations are involved, as the many real world examples in this book show

Analysis of survey data
Analysis of Survey Data
Analysis of Survey Data is concerned with statistical methods for the analysis of data collected from a survey. A survey could consist of data collected from a questionnaire or from measurements, such as those taken as part of a quality control process. Concerned with the statistical methods for the analysis of sample survey data, this book will update and extend the successful book edited by Skinner, Holt and Smith on ‘Analysis of Complex Surveys’. The focus will be on methodological issues, which arise when applying statistical methods to sample survey data and will discuss in detail the impact of complex sampling schemes. Further issues, such as how to deal with missing data and measurement of error will also be critically discussed.