Inference for Change-Point and Related Processes

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 patient’s 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. Read more at www.newton.ac.uk/programmes/ICP/

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A primal dual method for inverse problems in MRI with non-linear forward operators

Valkonen, T (University of Cambridge) Friday 07 February 2014, 14:30-15:00

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Applications of Change-Points Methods in Brain Signal and Image Analysis

Ombao, H (University of California, Irvine) Wednesday 05 February 2014, 11:30-12:30

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Wavelet-based Bayesian Estimation of Long Memory Models - an Application to fMRI Data

Vannucci, M (Rice University) Tuesday 04 February 2014, 14:00-15:00

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Detecting copy number variants for rare genetic disorders and non-invasive pre-natal diagnosis

Plagnol, V (University College London) Tuesday 04 February 2014, 11:30-12:30

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Methods for detecting graph based change points for fMRI and financial data

Cribben, I (University of Alberta) Wednesday 05 February 2014, 10:00-11:00

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Exact Bayesian inference for change point models with application to genomics

Robin, S (INRA - Institut National de la Recherche Agronomique) Monday 03 February 2014, 14:00-15:00

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Detection of Genomic Signals by Resequencing

Siegmund, D (Stanford University) Monday 03 February 2014, 11:30-12:30

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Theory and Inference for a Class of Nonlinear Models with Application to Time Series of Counts.

Davis, R (Columbia University) Thursday 30 January 2014, 11:30-12:30

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Modeling spatial nonstationarity and inference for exceedances in environmental applications.

Craigmile, P (Ohio State University) Tuesday 28 January 2014, 11:30-12:30

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Incorporating Geostrophic Wind Information for Improved Space-Time Short-Term Wind Speed Forecasting and Power System Dispatch.

Genton, M (King Abdullah University of Science and Technology (KAUST)) Wednesday 29 January 2014, 11:30-12:30

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Graph-Based Change-Point Detection

Chen, H (University of California, Davis) Tuesday 21 January 2014, 11:30-12:30

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Fourier based statistics for irregular spaced spatial data: with an application to testing for spatial stationarity.

Subba Rao , S (Texas A&M University ) Friday 24 January 2014, 14:00-15:00

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Robust monitoring of CAPM portfolios beta

Husková, M (Charles University, Prague) Wednesday 22 January 2014, 11:30-12:30

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Measuring dependence with local Gaussian correlation: Theory and applications.

Tjøstheim, D (Universitetet i Bergen) Thursday 23 January 2014, 11:30-12:30

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Statistical Inference for Panel Data

Horvath, L (University of Utah) Friday 24 January 2014, 12:00-13:00

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The Volatility of International Migration Flows: Estimating Past Trends in Nordic Countries and Lessons for Forecasting with Uncertainty.

Abel, G (Austrian Academy of Sciences) Tuesday 21 January 2014, 14:00-15:00

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Characterizing, predicting and handling rapid and large changes of wind power production.

Girard, R (Mines Paris Tech) Monday 27 January 2014, 15:10-16:10

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Precision of Disorders Detection

Szajowski, K (Wroclaw University of Technology) Wednesday 15 January 2014, 16:30-17:00

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An Automated Statistician which learns Bayesian nonparametric models of time series data

Ghahramani, Z (University of Cambridge) Thursday 16 January 2014, 14:15-15:00

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The group fused Lasso for multiple change-point detection

Vert, J-P (Mines ParisTech) Friday 17 January 2014, 09:30-10:15

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