Department of Statistical Science

Statistical Science

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General Theory and Methodology

The research carried out under this theme covers foundational and theoretical aspects of probability and inferential statistics, and generic statistical methodology. Current research interests include:

• Philosophical foundations of probability and statistics
• Theory of inference, including Bayesian theory, predictive inference and asymptotic theory
• Core Bayesian methodology
• Statistical methodology for causal inference
• Inference for stochastic models, nonparametric and semiparametric inference
• Methodology for multivariate data, including cluster analysis, multivariate calibration and classification
• Machine learning, classification, pattern recognition
• Decision analysis via operational research and financial methods

Theme members

Name	Position	Selected Applications	Keywords
Christian Hennig	Senior Lecturer (Theme Lead)	Various, currently including biogeography, musicology, social stratification	Cluster analysis, foundations of statistics and data analysis, robust statistics
Gianluca Baio	Lecturer	Evaluation of interventions, with specific interest in health care	Regression discontinuity designs in epidemiology
Richard Chandler	Senior Lecturer	Climatology, hydrology, environmental sciences	Inference for dependent data, estimating functions
Maria De Iorio	Reader
Tom Fearn	Professor	Near infrared spectroscopy	Multivariate analysis, including distance-based methods, visualisation, classification
Mark Girolami	Professor	Bioinformatics, systems biology	Multivariate calibration of nonlinear dynamic systems, theory and methodology
Ioannis Kosmidis	Lecturer		Bias reduction, simulation, clustering
Giampiero Marra	Lecturer	Socio-economic studies, biostatistics, air pollution	Semiparametric simultaneous equation estimation methods for studies affected by unobservable confounding, heterogeneity and sample selection
Sofia Olhede	Professor	Medical imaging, oceanography, ecology	Penalised likelihood
Yvo Pokern	Lecturer		Nonparametric estimation for diffusions, hypoelliptic diffusions, numerical aspects of Gaussian Markov Random Fields
Afzal Siddiqui	Senior Lecturer	Energy economics, risk management	Real options, optimisation
Jinghao Xue	Lecturer	Medical imaging	Pattern recognition, data mining, image processing
Phil Dawid	Honorary Professor	Evidential reasoning, legal and forensic statistics.	Foundations, causal inference, Bayesian decision-theory, predictive inference, information geometry
Trevor Sweeting	Emeritus Professor	Various, currently biological and medical applications, information retrieval systems	Foundations, Bayesian theory, predictive inference, asymptotic theory
Ben Calderhead	Postdoctoral Research Fellow
Simon Harden	PhD Student
Joanna Kaczmarska	PhD Student
Xiao M. Ken Liang	PhD Student
Daniel Meddings	PhD Student
Ying Zhang	PhD Student

Recent research grants

• 2012-2013. EPSRC. Semiparametric Sample Selection Modelswith Applications in Biostatistics, Economics and Environmetrics (Marra)
• 2010-2014. European Commission FP7. Energy Efficiency and Risk Management in Public Buildings (EnRiMa).  (Siddiqui, jointly with European collaborators)  
• 2010-2013.  BBSRC.  Inference-based Modelling in Population and Systems Biology (Girolami)
• 2008-2011. Mid-Norway Business Research Fund, Statkraft, and Trønder Energi. Financial Engineering Analysis of Electricity Spot and Derivatives Markets (ELDEV).  (Siddiqui, jointly with NTNU and Trondheim Business School)
• 2007–2011.  EPSRC. Advancing Machine Learning Methodology for New Classes of Prediction Problems.  (Girolami)
• 2007-2011.  EPSRC. London Taught Course Centre.
• 2006-2009.  EPSRC.  Geometrical Methods for Statistical Inference and Decision. (Parry (RF) and Dawid).

(EP/E504485/1)