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moving production papers to published papers
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jchiquet committed Sep 10, 2024
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86 changes: 0 additions & 86 deletions _bibliography/in_production.bib
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@article{pishchagina2024,
bibtex_show = {true},
author = {Pishchagina, Liudmila and Rigaill, Guillem and Runge,
Vincent},
publisher = {French Statistical Society},
title = {Geometric-Based {Pruning} {Rules} for {Change} {Point}
{Detection} in {Multiple} {Independent} {Time} {Series}},
journal = {Computo},
year = 2024,
url = {https://computo.sfds.asso.fr/published-202406-pishchagina-change-point/},
doi = {10.57750/9vvx-eq57},
issn = {2824-7795},
type = {{Research article}},
domain = {Statistics},
language = {R},
repository = {published-202406-pishchagina-change-point},
langid = {en},
abstract = {We address the challenge of identifying multiple change
points in a group of independent time series, assuming these change
points occur simultaneously in all series and their number is
unknown. The search for the best segmentation can be expressed as a
minimization problem over a given cost function. We focus on dynamic
programming algorithms that solve this problem exactly. When the
number of changes is proportional to data length, an
inequality-based pruning rule encoded in the PELT algorithm leads to
a linear time complexity. Another type of pruning, called functional
pruning, gives a close-to-linear time complexity whatever the number
of changes, but only for the analysis of univariate time series. We
propose a few extensions of functional pruning for multiple
independent time series based on the use of simple geometric shapes
(balls and hyperrectangles). We focus on the Gaussian case, but some
of our rules can be easily extended to the exponential family. In a
simulation study we compare the computational efficiency of
different geometric-based pruning rules. We show that for a small
number of time series some of them ran significantly faster than
inequality-based approaches in particular when the underlying number
of changes is small compared to the data length.}
}

@article{legrand2024,
bibtex_show = {true},
author = {Legrand, Juliette and Pimont, François and Dupuy, Jean-Luc
and Opitz, Thomas},
publisher = {French Statistical Society},
title = {Bayesian Spatiotemporal Modelling of Wildfire Occurrences and
Sizes for Projections Under Climate Change},
journal = {Computo},
year = 2024,
url = {https://computo.sfds.asso.fr/published-202407-legrand-wildfires/},
doi = {10.57750/4y84-4t68},
issn = {2824-7795},
type = {{Research article}},
domain = {Statistics},
language = {R},
repository = {published-202407-legrand-wildfires},
langid = {en},
abstract = {Appropriate spatiotemporal modelling of wildfire activity
is crucial for its prediction and risk management. Here, we focus on
wildfire risk in the Aquitaine region in the Southwest of France and
its projection under climate change. We study whether wildfire risk
could further increase under climate change in this specific region,
which does not lie in the historical core area of wildfires in
Southeastern France, corresponding to the Southwest. For this
purpose, we consider a marked spatiotemporal point process, a
flexible model for occurrences and magnitudes of such environmental
risks, where the magnitudes are defined as the burnt areas. The
model is first calibrated using 14 years of past observation data of
wildfire occurrences and weather variables, and then applied for
projection of climate-change impacts using simulations of numerical
climate models until 2100 as new inputs. We work within the
framework of a spatiotemporal Bayesian hierarchical model, and we
present the workflow of its implementation for a large dataset at
daily resolution for 8km-pixels using the INLA-SPDE approach. The
assessment of the posterior distributions shows a satisfactory fit
of the model for the observation period. We stochastically simulate
projections of future wildfire activity by combining climate model
output with posterior simulations of model parameters. Depending on
climate models, spline-smoothed projections indicate low to moderate
increase of wildfire activity under climate change. The increase is
weaker than in the historical core area, which we attribute to
different weather conditions (oceanic versus Mediterranean). Besides
providing a relevant case study of environmental risk modelling,
this paper is also intended to provide a full workflow for
implementing the Bayesian estimation of marked log-Gaussian Cox
processes using the R-INLA package of the R statistical software.}
}
87 changes: 87 additions & 0 deletions _bibliography/published.bib
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Original file line number Diff line number Diff line change
@@ -1,3 +1,90 @@
@article{legrand2024,
bibtex_show = {true},
author = {Legrand, Juliette and Pimont, François and Dupuy, Jean-Luc
and Opitz, Thomas},
publisher = {French Statistical Society},
title = {Bayesian Spatiotemporal Modelling of Wildfire Occurrences and
Sizes for Projections Under Climate Change},
journal = {Computo},
year = 2024,
url = {https://computo.sfds.asso.fr/published-202407-legrand-wildfires/},
doi = {10.57750/4y84-4t68},
issn = {2824-7795},
type = {{Research article}},
domain = {Statistics},
language = {R},
repository = {published-202407-legrand-wildfires},
langid = {en},
abstract = {Appropriate spatiotemporal modelling of wildfire activity
is crucial for its prediction and risk management. Here, we focus on
wildfire risk in the Aquitaine region in the Southwest of France and
its projection under climate change. We study whether wildfire risk
could further increase under climate change in this specific region,
which does not lie in the historical core area of wildfires in
Southeastern France, corresponding to the Southwest. For this
purpose, we consider a marked spatiotemporal point process, a
flexible model for occurrences and magnitudes of such environmental
risks, where the magnitudes are defined as the burnt areas. The
model is first calibrated using 14 years of past observation data of
wildfire occurrences and weather variables, and then applied for
projection of climate-change impacts using simulations of numerical
climate models until 2100 as new inputs. We work within the
framework of a spatiotemporal Bayesian hierarchical model, and we
present the workflow of its implementation for a large dataset at
daily resolution for 8km-pixels using the INLA-SPDE approach. The
assessment of the posterior distributions shows a satisfactory fit
of the model for the observation period. We stochastically simulate
projections of future wildfire activity by combining climate model
output with posterior simulations of model parameters. Depending on
climate models, spline-smoothed projections indicate low to moderate
increase of wildfire activity under climate change. The increase is
weaker than in the historical core area, which we attribute to
different weather conditions (oceanic versus Mediterranean). Besides
providing a relevant case study of environmental risk modelling,
this paper is also intended to provide a full workflow for
implementing the Bayesian estimation of marked log-Gaussian Cox
processes using the R-INLA package of the R statistical software.}
}

@article{pishchagina2024,
bibtex_show = {true},
author = {Pishchagina, Liudmila and Rigaill, Guillem and Runge,
Vincent},
publisher = {French Statistical Society},
title = {Geometric-Based {Pruning} {Rules} for {Change} {Point}
{Detection} in {Multiple} {Independent} {Time} {Series}},
journal = {Computo},
year = 2024,
url = {https://computo.sfds.asso.fr/published-202406-pishchagina-change-point/},
doi = {10.57750/9vvx-eq57},
issn = {2824-7795},
type = {{Research article}},
domain = {Statistics},
language = {R},
repository = {published-202406-pishchagina-change-point},
langid = {en},
abstract = {We address the challenge of identifying multiple change
points in a group of independent time series, assuming these change
points occur simultaneously in all series and their number is
unknown. The search for the best segmentation can be expressed as a
minimization problem over a given cost function. We focus on dynamic
programming algorithms that solve this problem exactly. When the
number of changes is proportional to data length, an
inequality-based pruning rule encoded in the PELT algorithm leads to
a linear time complexity. Another type of pruning, called functional
pruning, gives a close-to-linear time complexity whatever the number
of changes, but only for the analysis of univariate time series. We
propose a few extensions of functional pruning for multiple
independent time series based on the use of simple geometric shapes
(balls and hyperrectangles). We focus on the Gaussian case, but some
of our rules can be easily extended to the exponential family. In a
simulation study we compare the computational efficiency of
different geometric-based pruning rules. We show that for a small
number of time series some of them ran significantly faster than
inequality-based approaches in particular when the underlying number
of changes is small compared to the data length.}
}

@article{susmann_adaptive,
bibtex_show = {true},
author = {Susmann, Herbert and and Chambaz, Antoine and Josse, Julie},
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