Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data ModellingCitation formats

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@article{221369187fc044669e8ffbdab688878b,
title = "Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data Modelling",
abstract = "We consider the situation where a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. We develop a Bayesian approach to infer the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) sampler is constructed for approximating the posterior distribution. Our method is benchmarked using simulated data and is applied to uncover differences in the dynamics of fungal growth from imaging time course data collected from different strains. The source code is available on CRAN.",
author = "Panagiotis Papastamoulis and Takanori Furukawa and {van Rhijn}, Norman and Michael Bromley and Elaine Bignell and Magnus Rattray",
year = "2019",
doi = "10.1515/ijb-2018-0052",
language = "English",
journal = "International Journal of Biostatistics",
issn = "1557-4679",
publisher = "Berkeley Electronic Press",

}

RIS

TY - JOUR

T1 - Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data Modelling

AU - Papastamoulis, Panagiotis

AU - Furukawa, Takanori

AU - van Rhijn, Norman

AU - Bromley, Michael

AU - Bignell, Elaine

AU - Rattray, Magnus

PY - 2019

Y1 - 2019

N2 - We consider the situation where a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. We develop a Bayesian approach to infer the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) sampler is constructed for approximating the posterior distribution. Our method is benchmarked using simulated data and is applied to uncover differences in the dynamics of fungal growth from imaging time course data collected from different strains. The source code is available on CRAN.

AB - We consider the situation where a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. We develop a Bayesian approach to infer the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) sampler is constructed for approximating the posterior distribution. Our method is benchmarked using simulated data and is applied to uncover differences in the dynamics of fungal growth from imaging time course data collected from different strains. The source code is available on CRAN.

U2 - 10.1515/ijb-2018-0052

DO - 10.1515/ijb-2018-0052

M3 - Article

JO - International Journal of Biostatistics

JF - International Journal of Biostatistics

SN - 1557-4679

ER -