Statistical Science


Professor Richard Chandler

PositionProfessor of Statistics, Head of Department
Phone (external)+44 (0)20 7679 1880+44 (0)20 7679 1880
Phone (internal)41880
Personal webpagehttp://www.homepages.ucl.ac.uk/~ucakarc/
ThemesStochastic Modelling and Time Series; General Theory and Methodology

* @ucl.ac.uk

Biographical Details

Richard is a Professor and the current Head of Department in the Department of Statistical Science at University College London, where he has worked since completing his PhD at UMIST in 1994. He is a Fellow of the Royal Statistical Society, member of the Bernoulli and International Environmetric Societies and an honorary member of the Institute of Atmospheric Physics in Beijing. From 2011 to 2014 he was Joint Editor for the Journal of the Royal Statistical Society, Series C (Applied Statistics). He leads the RACER (Robust Assessment and Communication of Environmental Risk) consortium, funded by the Natural Environment Research Council under their PURE (Probability, Uncertainty and Risk in the Environment) programme. He currently serves on the committees of the International Meeting series on Statistical Climatology and the surfacetemperatures.org project.

Research Interests

Richard has extensive experience of developing and applying statistical methods for the environmental sciences. Particular interests include the analysis of time series and space-time data, with application areas including hydrology and the impacts of climate change.

Selected publications

  • Chandler R.E. (2013). Exploiting strength, discounting weakness: combining information from multiple climate simulators. Phil Trans R Soc A 371: 20120388, doi: 10.1098/rsta.2012.0388.
  • Maraun, D., F. Wetterhall, A.M. Ireson, R. E. Chandler, E. J. Kendon, M. Widmann, S. Brienen, H.W. Rust, T. Sauter, M. Themeßl, V.K.C. Venema, K.P. Chun, C.M. Goodess, R.G. Jones, C. Onof, M. Vrac, and I. Thiele-Eich (2010). Precipitation downscaling under climate change - recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48, RG3003, 34pp. doi: 10.1029/2009RG000314.
  • Jesus, J. and R.E. Chandler (2011). Estimating functions and the generalized method of moments. Interface Focus, 1(6), 871-885, DOI: 10.1098/rsfs.2011.0057.
  • Chandler, R.E. and E.M. Scott (2011). Statistical Methods for Trend Detection and Analysis in the Environmental Sciences. Wiley, Chichester. Data sets and software available from here.
  • Leith, N.A. and Chandler, R.E. (2010). A framework for interpreting climate model outputs. J. R. Statist. Soc. C, 59(2), pp. 279-296.
  • Chandler, R.E. and Bate, S. (2007). Inference for clustered data using the independence log-likelihood. Biometrika 94, pp. 167-183. doi:0.1093/biomet/asm015.
  • Yang, C., Chandler, R.E., Isham, V. and Wheater, H.S. (2005). Spatial-temporal rainfall simulation using Generalized Linear Models. Water Resources Research 41, doi:10.1029/2004WR003739.
  • Chandler, R.E. (2005). On the use of generalized linear models for interpreting climate variability. Environmetrics 16(7), pp. 699-715. doi:10.1002/env.731.