Inference for Change-Point and Related Processes

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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 variance, of the data will change along data. This feature of data is known as non-stationarity. An important a…

Cambridge University


    • Feb 14, 2014 LATEST EPISODE
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    Latest episodes from Inference for Change-Point and Related Processes

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

    Play Episode Listen Later Feb 14, 2014 29:32


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

    forward method dual primal operators nonlinear t university inverse problems cambridge friday
    Applications of Change-Points Methods in Brain Signal and Image Analysis

    Play Episode Listen Later Feb 12, 2014 56:55


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

    california brain points methods applications signal
    Wavelet-based Bayesian Estimation of Long Memory Models - an Application to fMRI Data

    Play Episode Listen Later Feb 12, 2014 58:36


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

    data memory models application fmri bayesian estimation vannucci wavelet
    Detecting copy number variants for rare genetic disorders and non-invasive pre-natal diagnosis

    Play Episode Listen Later Feb 12, 2014 59:38


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

    rare diagnosis copy variants invasive prenatal detecting genetic disorders
    Methods for detecting graph based change points for fMRI and financial data

    Play Episode Listen Later Feb 12, 2014 52:30


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

    points methods graphs detecting fmri financial data
    Exact Bayesian inference for change point models with application to genomics

    Play Episode Listen Later Feb 12, 2014 54:19


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

    models application exact genomics bayesian inference
    Detection of Genomic Signals by Resequencing

    Play Episode Listen Later Feb 11, 2014 61:00


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

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

    Play Episode Listen Later Feb 5, 2014 61:00


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

    class theory models application counts nonlinear inference time series
    Modeling spatial nonstationarity and inference for exceedances in environmental applications.

    Play Episode Listen Later Feb 5, 2014 67:00


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

    environmental applications modeling spatial inference
    Incorporating Geostrophic Wind Information for Improved Space-Time Short-Term Wind Speed Forecasting and Power System Dispatch.

    Play Episode Listen Later Feb 5, 2014 64:00


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

    science system improved incorporating short term forecasting dispatch spacetime wind speed
    Graph-Based Change-Point Detection

    Play Episode Listen Later Feb 3, 2014 69:00


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

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

    Play Episode Listen Later Feb 3, 2014 61:00


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

    data testing application statistics spatial irregular spaced fourier m university friday
    Robust monitoring of CAPM portfolios beta

    Play Episode Listen Later Feb 3, 2014 65:00


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

    beta monitoring robust portfolios capm
    Measuring dependence with local Gaussian correlation: Theory and applications.

    Play Episode Listen Later Feb 3, 2014 61:00


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

    local theory applications measuring dependence correlation gaussian
    Statistical Inference for Panel Data

    Play Episode Listen Later Feb 3, 2014 62:00


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

    data panel statistical horvath inference utah friday
    The Volatility of International Migration Flows: Estimating Past Trends in Nordic Countries and Lessons for Forecasting with Uncertainty.

    Play Episode Listen Later Feb 3, 2014 47:25


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

    lessons uncertainty flows forecasting volatility estimating international migration nordic countries sciences tuesday
    Characterizing, predicting and handling rapid and large changes of wind power production.

    Play Episode Listen Later Jan 28, 2014 72:00


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

    production large rapid predicting girard wind power characterizing
    Precision of Disorders Detection

    Play Episode Listen Later Jan 27, 2014 26:07


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

    disorders precision detection technology wednesday
    An Automated Statistician which learns Bayesian nonparametric models of time series data

    Play Episode Listen Later Jan 24, 2014 60:00


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

    data models automated learns bayesian statisticians time series cambridge thursday
    The group fused Lasso for multiple change-point detection

    Play Episode Listen Later Jan 24, 2014 46:57


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

    detection vert lasso fused
    Quickest Changepoint Detection: Optimality Properties of the Shiryaev-Roberts-Type Procedures

    Play Episode Listen Later Jan 24, 2014 44:23


    Tartakovsky , A (University of Connecticut) Friday 17 January 2014, 10:15-11:00

    university roberts properties procedures detection quickest tartakovsky optimality
    Bayesian inference in continuous time jump processes

    Play Episode Listen Later Jan 24, 2014 44:32


    Godsill, S (University of Cambridge) Thursday 16 January 2014, 13:30-14:15

    processes continuous bayesian inference time jump s university cambridge thursday
    Adaptive Spectral Estimation for Nonstationary Time Series

    Play Episode Listen Later Jan 24, 2014 39:12


    Stoffer, D (University of Pittsburgh) Friday 17 January 2014, 11:30-12:15

    adaptive spectral estimation time series stoffer
    Computationally Efficient Algorithms for Detecting Changepoints

    Play Episode Listen Later Jan 24, 2014 39:43


    Fearnhead, P (Lancaster University) Thursday 16 January 2014, 09:30-10:00

    algorithms efficient detecting
    An algorithm to segment count data using a binomial negative model

    Play Episode Listen Later Jan 24, 2014 32:23


    Rigaill, G (INRA-CNRS-Université d'Evry Val d'Essonne, URGV) Thursday 16 January 2014, 10:00-10:30

    data model negative algorithms essonne binomial
    Locally-stationary modelling of oceanographic spatiotemporal data

    Play Episode Listen Later Jan 24, 2014 27:41


    Sykulski, AM (University College London) Thursday 16 January 2014, 16:10-16:30

    data locally modelling stationary spatiotemporal oceanographic
    Simultaneous break point detection and variable selection in quantile regression models

    Play Episode Listen Later Jan 24, 2014 27:37


    Aue, A (UC Davis) Thursday 16 January 2014, 10:30-11:00

    models selection detection regression variable simultaneous breakpoint aue quantile
    Local moving Fourier based bootstrapping

    Play Episode Listen Later Jan 24, 2014 26:04


    Lindner, F (Karlsruhe Institute of Technology) Thursday 16 January 2014, 15:30-15:50

    moving local bootstrapping lindner fourier technology thursday
    Shape smoothing (and what I hope to get from the Newton change-point program)

    Play Episode Listen Later Jan 24, 2014 29:02


    Heaton, T (Sheffield University) Wednesday 15 January 2014, 16:00-16:30

    shape newton heaton smoothing point program
    Multiscale Change Point Inference

    Play Episode Listen Later Jan 22, 2014 55:53


    Munk, A (University of Goettingen) Tuesday 14 January 2014, 11:30-12:15

    university munk inference multiscale
    Modelling multivariate nonstationarity

    Play Episode Listen Later Jan 22, 2014 46:42


    Olhede, S (University College London) Wednesday 15 January 2014, 11:30-12:15

    modelling multivariate
    Wild Binary Segmentation for multiple change-point detection

    Play Episode Listen Later Jan 22, 2014 43:02


    Fryzlewicz, P (London School of Economics) Tuesday 14 January 2014, 13:30-14:10

    wild detection binary segmentation
    Detection of multiple structural breaks in multivariate time series

    Play Episode Listen Later Jan 22, 2014 39:21


    Dette, H (Ruhr-Universität Bochum) Tuesday 14 January 2014, 14:50-15:30

    breaks dette detection structural time series multivariate
    Change-point tests based on estimating functions

    Play Episode Listen Later Jan 22, 2014 35:55


    Kirch, C (Karlsruhe Institute of Technology) Wednesday 15 January 2014, 13:30-14:00

    tests functions estimating kirch technology wednesday
    Non-stationary functional time series: an application to electricity supply and demand

    Play Episode Listen Later Jan 22, 2014 34:40


    Eichler, M (Universiteit Maastricht) Wednesday 15 January 2014, 14:30-15:00

    application functional electricity supply and demand stationary time series eichler
    Inference for multiple change-points in time series via likelihood ratio scan statistics

    Play Episode Listen Later Jan 22, 2014 29:45


    Yau, C-Y (Chinese University of Hong Kong) Wednesday 15 January 2014, 14:00-14:30

    points statistics ratio scan likelihood inference time series yau
    High Dimensional Stochastic Regression with Latent Factors,Endogeneity and Nonlinearity

    Play Episode Listen Later Jan 22, 2014 32:35


    Yao, Q (London School of Economics) Tuesday 14 January 2014, 14:10-14:50

    factors regression dimensional latent yao stochastic
    Nonparametric change-point detection with sparse alternatives

    Play Episode Listen Later Jan 22, 2014 31:00


    Harchaoui, Z (INRIA and LJK) Wednesday 15 January 2014, 10:30-11:00

    alternatives detection sparse
    Robust estimation of time-varying correlations

    Play Episode Listen Later Jan 22, 2014 32:25


    Motta, G (Columbia University) Tuesday 14 January 2014, 10:30-11:00

    robust motta varying estimation correlations
    Heteroscedasticity and Autocorrelation Robust Structural Change Detection

    Play Episode Listen Later Jan 22, 2014 28:19


    Zhou, Z (University of Toronto) Tuesday 14 January 2014, 16:00-16:30

    robust zhou structural change change detection
    Decentralized Quickest Change Detection in Hidden Markov Models for Sensor Networks

    Play Episode Listen Later Jan 22, 2014 28:35


    Fuh, C-D (National Central University, Taiwan) Wednesday 15 January 2014, 10:00-10:30

    hidden models networks decentralized sensor quickest markov fuh change detection
    Detecting smooth changes in locally stationary processes

    Play Episode Listen Later Jan 22, 2014 31:33


    Vogt, M (University of Konstanz) Tuesday 14 January 2014, 09:30-10:00

    smooth processes locally m university detecting vogt stationary

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