Statistical Methodology

Statistical Methodology

Staff

Dr Andrej Aderhold : Research Associate

Supervisor: Dirk Husmeier

  • Publications
  • Dr Agnieszka Borowska : Research Assistant

    Member of other research groups: Scholarship of Learning and Teaching in Statistics, Environmental Statistics, Biostatistics and Statistical Genetics
    Supervisor: Dirk Husmeier

  • Dr Charalampos Chanialidis : Lecturer

    Member of other research groups: Scholarship of Learning and Teaching in Statistics

  • Personal Website
  • Publications
  • Dr Nema Dean : Lecturer

    Supervised and unsupervised learning; mixture models; variable selection; educational testing data; dynamic treatment regime estimation

    Research student: Cunyi Wang

  • Personal Website
  • Publications
  • Dr Ludger Evers : Lecturer (part-time)

    Statistical methods in machine learning; partition and mixture-based models; non-linear dimension reduction; efficient computational strategies for data analysis

    Member of other research groups: Scholarship of Learning and Teaching in Statistics
    Research staff: Marnie McLean
    Research students: Craig Alexander, Benjamin Szili, Ivona Voroneckaja, Dimitra Eleftheriou, Ashwini Venkatasubramaniam

  • Publications
  • Dr Mayetri Gupta : Reader

    Bayesian methodology; MCMC and Monte Carlo methods;  Clustering, classification and model selection; Statistical methods for computational biology

    Member of other research groups: Biostatistics and Statistical Genetics

  • Personal Website
  • Publications
  • Prof Dirk Husmeier : Chair of Statistics

    Machine learning and Bayesian statistics applied to systems biology and bioinformatics; Bayesian networks; statistical phylogenetics

    Research staff: Andrej Aderhold, Agnieszka Borowska, Benn Macdonald
    Research students: Diana Giurghita, Alan Lazarus, Luisa Paun, Ionut Paun, Katie Stewart

  • Personal Website
  • Publications
  • Dr Dimitra Kosta : LKAS Fellowship

    Algebraic statistics; maximum likelihood estimation; log-linear or toric models; statistical inference using numerical algebraic geometry. 

    Member of other research groups: Environmental Statistics, Biostatistics and Statistical Genetics, Geometry and Topology, Algebra

  • Personal Website
  • Publications
  • Dr Vincent Macaulay : Reader

    Statistical genetics; population genetics; Bayesian methods; phylogenetics

    Member of other research groups: Biostatistics and Statistical Genetics
    Research students: Flynn Gewirtz-O'Reilly, Suzy Whoriskey

  • Personal Website
  • Publications
  • Dr Benn Macdonald : Research Assistant

    Member of other research groups: Mathematical Biology
    Supervisor: Dirk Husmeier

  • Dr Agostino Nobile : Honorary Research Fellow

    Bayesian statistics; MCMC and other Monte Carlo methods; mixture models; discrete choice models

  • Personal Website
  • Publications
  • Dr Theo Papamarkou : Lecturer

    Research students: Benjamin Szili, Dimitra Eleftheriou

  • Dr Surajit Ray : Senior lecturer

    Functional Data Analysis; Analysis of mixture models; high-dimensional data; medical image analysis; analysis of earth systems data; immunoinformatics

    Member of other research groups: Environmental Statistics, Biostatistics and Statistical Genetics
    Research students: Maryam Al Alawi , Salihah Alghamdi, Bader Lafi Q Alruwaili, Flynn Gewirtz-O'Reilly
    Postgraduate opportunities: Analysis of Spatially correlated functional data objects.

  • Personal Website
  • Publications
  • Prof Michael Titterington : Honorary Senior Research Fellow

    Statistical analysis of mixture distributions; latent structure analysis; pattern recognition; machine learning; smoothing and nonparametric statistics; optimum design of experiments

  • Personal Website
  • Publications
  • Dr Bernard Torsney : Honorary Research Fellow

    Non-parametric inference; optimisation; optimal experimental design; sampling theory; applications in economics; multiple comparisons

  • Personal Website
  • Publications
  • Dr Vlad Vyshemirsky : Lecturer

    Member of other research groups: Biostatistics and Statistical Genetics
    Research students: Randa Alharbi, Lida Mavrogonatou

  • Publications

  • Postgraduates

    Postgraduate opportunities

    Analysis of Spatially correlated functional data objects. (PhD)

    Supervisors: Surajit Ray
    Relevant research groups: Environmental Statistics, Statistical Methodology

    Historically, functional data analysis techniques have widely been used to analyze traditional time series data, albeit from a different perspective. Of late, FDA techniques are increasingly being used in domains such as environmental science, where the data are spatio-temporal in nature and hence is it typical to consider such data as functional data where the functions are correlated in time or space. An example where modeling the dependencies is crucial is in analyzing remotely sensed data observed over a number of years across the surface of the earth, where each year forms a single functional data object. One might be interested in decomposing the overall variation across space and time and attribute it to covariates of interest. Another interesting class of data with dependence structure consists of weather data on several variables collected from balloons where the domain of the functions is a vertical strip in the atmosphere, and the data are spatially correlated. One of the challenges in such type of data is the problem of missingness, to address which one needs develop appropriate spatial smoothing techniques for spatially dependent functional data. There are also interesting design of experiment issues, as well as questions of data calibration to account for the variability in sensing instruments. Inspite of the research initiative in analyzing dependent functional data there are several unresolved problems, which the student will work on:

    • robust statistical models for incorporating temporal and spatial dependencies in functional data
    • developing reliable prediction and interpolation techniques for dependent functional data
    • developing inferential framework for testing hypotheses related to simplified dependent structures
    • analysing sparsely observed functional data by borrowing information from neighbours
    • visualisation of data summaries associated with dependent functional data
    • Clustering of functional data