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Journal of Computational and Graphical Statistics Volume 22, 2013 - Issue 4
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Articles: Computational Statistics Pot Pourri

An Online Expectation–Maximization Algorithm for Changepoint Models

Sinan Yildirim Statistical Laboratory, School of Mathematics, University of Bristol, Bristol, BS8 1TW, UK
,
Sumeetpal S. Singh Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK
&
Arnaud Doucet Department of Statistics, University of Oxford, Oxford, OX1 3TG, UK
Pages 906-926 | Received 01 Oct 2011, Published online: 21 Oct 2013

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Lingzhe Guo & Reza Modarres. (2022) Two multivariate online change detection models. Journal of Applied Statistics 49:2, pages 427-448.
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Articles from other publishers (21)

Mattia Stival, Mauro Bernardi & Petros Dellaportas. (2023) Doubly-online changepoint detection for monitoring health status during sports activities. The Annals of Applied Statistics 17:3.
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Beniamino Hadj-Amar, Jack Jewson & Mark Fiecas. (2023) Bayesian Approximations to Hidden Semi-Markov Models for Telemetric Monitoring of Physical Activity. Bayesian Analysis 18:2.
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Anastasia Kadochnikova & Visakan Kadirkamanathan. (2023) Estimation of potential field environments from heterogeneous behaviour of sensing agents. IET Signal Processing 17:1.
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Michalis K. Titsias, Jakub Sygnowski & Yutian Chen. (2022) Sequential changepoint detection in neural networks with checkpoints. Statistics and Computing 32:2.
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Mark P. Balenzuela, Adrian G. Wills, Christopher Renton & Brett Ninness. (2022) Parameter estimation for Jump Markov Linear Systems. Automatica 135, pages 109949.
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Mahdi Imani & Seyede Fatemeh Ghoreishi. (2021) Adaptive Real-Time Filter for Partially-Observed Boolean Dynamical Systems. Adaptive Real-Time Filter for Partially-Observed Boolean Dynamical Systems.
Lu Shaochuan. (2021) Bayesian Multiple Changepoint Detection for Stochastic Models in Continuous Time. Bayesian Analysis 16:2.
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Donna Henderson & Gerton Lunter. (2019) Efficient inference in state-space models through adaptive learning in online Monte Carlo expectation maximization. Computational Statistics 35:3, pages 1319-1344.
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Panagiotis Papastamoulis, Takanori Furukawa, Norman van Rhijn, Michael Bromley, Elaine Bignell & Magnus Rattray. (2020) Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data Modelling. The International Journal of Biostatistics 16:1.
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Kang-Ping Lu & Shao-Tung Chang. (2018) A fuzzy classification approach to piecewise regression models. Applied Soft Computing 69, pages 671-688.
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Eric Ruggieri. (2017) A pruned recursive solution to the multiple change point problem. Computational Statistics 33:2, pages 1017-1045.
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Kang-Ping Lu & Shao-Tung Chang. (2016) Detecting change-points for shifts in mean and variance using fuzzy classification maximum likelihood change-point algorithms. Journal of Computational and Applied Mathematics 308, pages 447-463.
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Eric Ruggieri & Marcus Antonellis. (2016) An exact approach to Bayesian sequential change point detection. Computational Statistics & Data Analysis 97, pages 71-86.
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Ye Tao, Rudolph Triebel & Daniel Cremers. (2015) Semi-supervised online learning for efficient classification of objects in 3D data streams. Semi-supervised online learning for efficient classification of objects in 3D data streams.
Nikolas Kantas, Arnaud Doucet, Sumeetpal S. Singh, Jan Maciejowski & Nicolas Chopin. (2015) On Particle Methods for Parameter Estimation in State-Space Models. Statistical Science 30:3.
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Elena Ehrlich, Ajay Jasra & Nikolas Kantas. (2013) Gradient Free Parameter Estimation for Hidden Markov Models with Intractable Likelihoods. Methodology and Computing in Applied Probability 17:2, pages 315-349.
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Emre Ozkan, Fredrik Lindsten, Carsten Fritsche & Fredrik Gustafsson. (2015) Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems. IEEE Transactions on Signal Processing 63:3, pages 754-765.
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Andreas Svensson, Thomas B. Schon & Fredrik Lindsten. (2014) Identification of jump Markov linear models using particle filters. Identification of jump Markov linear models using particle filters.
Marziyeh Keshavarz & Biao Huang. (2014) Expectation Maximization method for multivariate change point detection in presence of unknown and changing covariance. Computers & Chemical Engineering 69, pages 128-146.
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Christopher Nemeth, Paul Fearnhead & Lyudmila Mihaylova. (2014) Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments. IEEE Transactions on Signal Processing 62:5, pages 1245-1255.
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Aditya Tulsyan, Biao Huang, R. Bhushan Gopaluni & J. Fraser Forbes. (2013) On simultaneous on-line state and parameter estimation in non-linear state-space models. Journal of Process Control 23:4, pages 516-526.
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