Dr Matthew NunesLecturer in Statistics
My research is primarily in the area of developing multiscale methods for analyzing signals, such as time series and images. These methods capture information in signals by examining them at different scales or frequencies via wavelet transforms. I am particularly interested in applications of wavelet lifting schemes in non-standard data collection situations, for example irregular sampling regimes in time or space.
PhD Supervision Interests
I am interested in supervising PhD students in the broad areas of time series and image analysis, as well as multiscale (wavelet) methods in statistics and analysis of network data. Example research questions include: 1. Modelling irregularly spaced time series. Many time series have a natural irregular sampling structure, or feature missingness. For example, this could be due to faulty measurement devices or infrequent event data from environmental processes. Many models do not properly take this structure into account, which can lead to inaccurate modelling and conclusions being drawn. This project would focus on developing new models for such data. 2. Long memory. It has been well established that many time series, from physiological data to climatic series exhibit long memory, i.e. a dependence structure which lasts over long periods. Accurate estimation of measures of persistence can be useful in climate modelling, however, traditional methods often suffer from bias. We would work on new methods of efficient estimation of these dependence measures in a range of time series and image settings. 3. Network data. There has been an explosion of data on networks, from epidemiological processes, to social media. However, not much work has been done linking the dynamics of the network itself together with the process observed on the network. The combine elements from time series and network analysis for computationally efficient inference methods for dynamic processes.
Multivariate locally stationary 2D wavelet processes with application to colour texture analysis
Taylor, S., Eckley, I.A., Nunes, M.A. 07/2017 In: Statistics and Computing. 27, 4, p. 1129-1143. 15 p.
Discussion of "Should we sample more frequently?: decision support via multirate spectrum estimation" by G. P. Nason, B. Powell, D. Elliott and P. A. Smith
Knight, M., Nunes, M.A. 02/2017 In: Journal of the Royal Statistical Society: Series A (Statistics in Society). 180, 2, p. 388-389. 2 p.
Complex-valued wavelet lifting and applications
Hamilton, J., Nunes, M.A., Knight, M., Fryzlewicz, P. 17/01/2017 In: Technometrics.
A wavelet lifting approach to long-memory estimation
Knight, M., Nason, G.P., Nunes, M.A. 3/09/2016 In: Statistics and Computing. 19 p.
abctools: an R package for tuning Approximate Bayesian Computation analyses
Nunes, M.A., Prangle, D. 12/2015 In: The R Journal. 7, 2, p. 189-205. 17 p.
Review of Kolaczyk, E. D. and Csárdi, G. Statistical Analysis of Network Data with R
Nunes, M.A. 6/08/2015 In: Journal of Statistical Software. 66
Modelling and prediction of time series arising on a graph
Nunes, M., Knight, M., Nason, G.P. 4/06/2015 In: Modeling and Stochastic Learning for Forecasting in High Dimensions. New York : Springer International Publishing p. 183-192. 10 p. Electronic ISBN: 9783319187327.
A multiscale test of spatial stationarity for textured images in R
Nunes, M., Taylor, S., Eckley, I. 06/2014 In: The R Journal. 6, 1, p. 20-30. 11 p.
A test of stationarity for textured images
Taylor, S., Eckley, I., Nunes, M. 2014 In: Technometrics. 56, 3, p. 291-301. 11 p.
A comparative review of dimension reduction methods in approximate Bayesian computation
Blum, M.G.B., Nunes, M., Prangle, D., Sisson, S.A. 05/2013 In: Statistical Science. 28, 2, p. 189-208. 20 p.
Spectral estimation for locally stationary time series with missing observations
Knight, M.A., Nunes, M., Nason, G.P. 07/2012 In: Statistics and Computing. 22, 4, p. 877-895. 19 p.
On optimal selection of summary statistics for approximate Bayesian computation
Nunes, M.A., Balding, D.J. 2010 In: Statistical Applications in Genetics and Molecular Biology. 9, 1, 16 p.
A multiscale variance stabilization for binomial sequence proportion estimation
Nunes, M.A., Nason, G.P. 10/2009 In: Statistica Sinica. 19, 4, p. 1491-1510. 20 p.
An adaptive lifting algorithm and applications
Knight, M., Nunes, M. 2008 In: Proceedings of the 56th Session of the ISI. Instituto Nacional de Estatıstica p. 166-173. 8 p.
Adaptive lifting for nonparametric regression
Nunes, M.A., Knight, M.I., Nason, G.P. 06/2006 In: Statistics and Computing. 16, 2, p. 143-159. 17 p.