Inference for change-point and related processes
ICP
13 January 2014 to 7 February 2014
Programme theme
In many applications data is collected over time or can be ordered with respect to some other criteria (e.g. position along a chromosome). Often the statistical properties, such as mean or variance, of the data will change along data. This feature of data is known as non-stationarity. An important and challenging problem is to be able to model and infer how these properties change. Examples occur in environmental applications (e.g. detecting changes in ecological systems due to climatic conditions crossing some critical thresholds), signal processing (e.g. structural analysis of EEG signals), epidemiology (e.g. early detection of hospital infections from changes in patients antibody levels), bioinformatics (e.g. detecting changes in copy number variation), and finance (e.g. changing volatility). As technology advances, and ever larger and complex data are collected, the need to model changes in the statistical properties of the data, and the difficulty of making inference for these models increases.
Two possibilities for modelling non-stationarity are changepoint models and locally-stationary models. The former splits data into segments, where the statistical properties of the data is the same within a segment, but changes between segments. These are often appropriate for applications with abrupt changes. By comparison, locally-stationary models are based on stochastic processes where the statistical properties vary more smoothly. These two approaches are thus complementary. To date research within both areas is generally carried out independently of the other.
The main themes of the programme will be:
- to explore links and synergy between changepoint and locally-stationary models, methods for inference and the associate theory;
- to develop (computationally) efficient inference methods, that can be applied to large data, and complex models in modern scientific applications; and
- to develop associated statistical theory, that will impact on the application of these models and methods.
A focus of the programme will be the application of these methods, particularly within finance, energy and environment, and biomedical sciences.
The Institute kindly requests that any papers published as a result of this programme’s activities are credited as such. Please acknowledge the support of the Institute in your paper using the following text:
The author(s) would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support and hospitality during the programme Inference for change-point and related processes, where work on this paper was undertaken. This work was supported by EPSRC grant EP/K032208/1.
Click here to download the programme's final scientific report
Preprints
| Title | Year | Programme | Publication Date |
|---|---|---|---|
Automatic construction and natural-language description of nonparametric regression modelsAuthors: JR Lloyd, D Duvenaud, R Grosse, JB Tenenbaum, Zoubin Ghahramani |
2013 | ICP | 21 October 2016 |
On optimal multiple changepoint algorithms for large dataAuthors: Rob Maidstone, T Hocking, Guillem Rigaill, P Fearnhead |
2013 | ICP | 21 October 2016 |
Change points in high dimensional settingsAuthors: John Aston, Claudia Kirch |
2013 | ICP | 21 October 2016 |
On some distributed disorder detectionAuthors: Krzysztof Szajowski |
2013 | ICP | 21 October 2016 |
Workshops
Inference for change-point and related processes
13 January 2014 to 17 January 2014
Seminars
| Tuesday 21st January 2014 | |||
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| 11:30 to 12:30 | Room 2 | 
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| 14:00 to 15:00 | Room 2 |
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| Wednesday 22nd January 2014 | |||
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| 11:30 to 12:30 | Room 2 |
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| Thursday 23rd January 2014 | |||
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| 11:30 to 12:30 |
Measuring dependence with local Gaussian correlation: Theory and applications. |
Room 2 |
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| Friday 24th January 2014 | |||
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| 12:00 to 13:00 | Room 2 |
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| 14:00 to 15:00 | Room 2 |
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| Monday 27th January 2014 | |||
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| 14:00 to 15:00 | Room 1 | ||
| 15:10 to 16:10 |
Characterizing, predicting and handling rapid and large changes of wind power production. |
Room 1 |
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| Tuesday 28th January 2014 | |||
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| 11:30 to 12:30 |
Modeling spatial nonstationarity and inference for exceedances in environmental applications. |
Room 2 |
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| Wednesday 29th January 2014 | |||
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| 11:30 to 12:30 | Room 2 |
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| Thursday 30th January 2014 | |||
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| 11:30 to 12:30 |
Theory and Inference for a Class of Nonlinear Models with Application to Time Series of Counts. |
Room 2 |
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| Monday 3rd February 2014 | |||
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| 11:30 to 12:30 | Room 2 |
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| 14:00 to 15:00 |
Exact Bayesian inference for change point models with application to genomics |
Room 2 |
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| Tuesday 4th February 2014 | |||
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| 11:30 to 12:30 |
Detecting copy number variants for rare genetic disorders and non-invasive pre-natal diagnosis |
Room 2 |
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| 14:00 to 15:00 |
Wavelet-based Bayesian Estimation of Long Memory Models - an Application to fMRI Data |
Room 2 |
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| Wednesday 5th February 2014 | |||
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| 10:00 to 11:00 |
Methods for detecting graph based change points for fMRI and financial data |
Room 2 |
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| 11:30 to 12:30 |
Applications of Change-Points Methods in Brain Signal and Image Analysis |
Room 2 |
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Organisers
- John Aston University of Warwick
- Idris Eckley Lancaster University
- Paul Fearnhead Lancaster University
- George Sofronov Macquarie University
Supported By
