13 January 2014 to 17 January 2014
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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.
|Monday 13th January 2014|
|14:00 to 15:00||Room 1|
|Tuesday 14th January 2014|
|09:30 to 10:00||Room 1|
|10:00 to 10:30||Room 1|
|13:30 to 14:10||
Wild Binary Segmentation for multiple change-point detection
|14:10 to 14:50||
High Dimensional Stochastic Regression with Latent Factors,Endogeneity and Nonlinearity
|14:50 to 15:30||
Detection of multiple structural breaks in multivariate time series
|16:00 to 16:30||
Heteroscedasticity and Autocorrelation Robust Structural Change Detection
|Wednesday 15th January 2014|
|09:30 to 10:00||Room 1|
|10:00 to 10:30||
Decentralized Quickest Change Detection in Hidden Markov Models for Sensor Networks
|13:30 to 14:00||Room 1|
|14:00 to 14:30||
Inference for multiple change-points in time series via likelihood ratio scan statistics
|14:30 to 15:00||
Non-stationary functional time series: an application to electricity supply and demand
|15:30 to 16:00||
High-Dimensional Incremental Divisive Clustering under Population Drift
|16:00 to 16:30||
Shape smoothing (and what I hope to get from the Newton change-point program)
|16:30 to 17:00||Room 1|
|Thursday 16th January 2014|
|09:30 to 10:00||
Computationally Efficient Algorithms for Detecting Changepoints
|10:00 to 10:30||
An algorithm to segment count data using a binomial negative model
|10:30 to 11:00||
Simultaneous break point detection and variable selection in quantile regression models
|15:30 to 15:50||Room 1|
|16:10 to 16:30||
Locally-stationary modelling of oceanographic spatiotemporal data
|Friday 17th January 2014|
|10:15 to 11:00||
Quickest Changepoint Detection: Optimality Properties of the Shiryaev-Roberts-Type Procedures
|11:30 to 12:15||Room 1|
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