Inference for change-point and related processes
ICPW01
13 January 2014 to 17 January 2014
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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.
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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