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Matthew Nunes supervises 4 postgraduate research students. If these students have produced research profiles, these are listed below:

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Dr Matthew Nunes


Matthew Nunes

Lancaster University

Fylde College



Tel: +44 1524 593959

PhD supervision

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.

Research Interests


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. 

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