Inference for change-point and related processes
ICPW01
13 January 2014 to 17 January 2014
Workshop 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 autocovariance, of the data will change across the 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.
Two possibilities for modelling non-stationarity are change-point models and locally-stationary models. One of the goals of the workshop is to investigate and develop links between these two approaches.
This workshop will start with overview lectures that introduce change-point and locally-stationary models and approaches to inference for these models.
There are opportunities for participants to present their work as a talk or poster. If you wish to do so, please include a short abstract (max 1 page) with your application.
The application is closed
Timetable
| Monday 13th January 2014 | |||
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| 14:00 to 15:00 | Room 1 | 
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| Tuesday 14th January 2014 | |||
|---|---|---|---|
| 09:30 to 10:00 | Room 1 |
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| 10:00 to 10:30 | Room 1 |
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| 13:30 to 14:10 |
Wild Binary Segmentation for multiple change-point detection |
Room 1 |
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| 14:10 to 14:50 |
High Dimensional Stochastic Regression with Latent Factors,Endogeneity and Nonlinearity |
Room 1 |
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| 14:50 to 15:30 |
Detection of multiple structural breaks in multivariate time series |
Room 1 |
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| 16:00 to 16:30 |
Heteroscedasticity and Autocorrelation Robust Structural Change Detection |
Room 1 |
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| Wednesday 15th January 2014 | |||
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| 09:30 to 10:00 | Room 1 |
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| 10:00 to 10:30 |
Decentralized Quickest Change Detection in Hidden Markov Models for Sensor Networks |
Room 1 |
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| 13:30 to 14:00 | Room 1 |
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| 14:00 to 14:30 |
Inference for multiple change-points in time series via likelihood ratio scan statistics |
Room 1 |
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| 14:30 to 15:00 |
Non-stationary functional time series: an application to electricity supply and demand |
Room 1 |
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| 15:30 to 16:00 |
High-Dimensional Incremental Divisive Clustering under Population Drift |
Room 1 |
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| 16:00 to 16:30 |
Shape smoothing (and what I hope to get from the Newton change-point program) |
Room 1 |
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| 16:30 to 17:00 | Room 1 |
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| Thursday 16th January 2014 | |||
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| 09:30 to 10:00 |
Computationally Efficient Algorithms for Detecting Changepoints |
Room 1 |
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| 10:00 to 10:30 |
An algorithm to segment count data using a binomial negative model |
Room 1 |
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| 10:30 to 11:00 |
Simultaneous break point detection and variable selection in quantile regression models |
Room 1 |
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| 15:30 to 15:50 | Room 1 |
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| 16:10 to 16:30 |
Locally-stationary modelling of oceanographic spatiotemporal data |
Room 1 |
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| Friday 17th January 2014 | |||
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| 10:15 to 11:00 |
Quickest Changepoint Detection: Optimality Properties of the Shiryaev-Roberts-Type Procedures |
Room 1 |
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| 11:30 to 12:15 | Room 1 |
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Supported By
