papers in productions

Author: jchiquet

_bibliography/in_production.bib

@@ -1,16 +1,87 @@
 
-@article{lefort_peerannot,
+@article{pishchagina2024,
   bibtex_show = {true},
-  author      = {Lefort, Tanguy and Charlier, Benjamin and Joly, Alexis and Salmon, Joseph},
-  title       = {{Peerannot: classification for crowdsourced image datasets with Python}},
-  journal     = {Computo},
+  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,
-  abstract    = {Crowdsourcing is a quick and easy way to collect labels for large datasets, involving many workers. However, workers often disagree with each other. Sources of error can arise from the workers’ skills, but also from the intrinsic difficulty of the task. We present peerannot: a Python library for managing and learning from crowdsourced labels for classification. Our library allows users to aggregate labels from common noise models or train a deep learning-based classifier directly from crowdsourced labels. In addition, we provide an identification module to easily explore the task difficulty of datasets and worker capabilities.},
-  doi         = {10.57750/qmaz-gr91},
-  repository  = {published-202402-lefort-peerannot},
+  url = {https://computo.sfds.asso.fr/published-202406-pishchagina-change-point/},
+  doi = {10.57750/9vvx-eq57},
+  issn = {2824-7795},
   type        = {{Research article}},
-  language    = {Python},
-  domain      = {Machine Learning},
-  keywords    = {crowdsourcing, label noise, task difficulty, worker ability, classification},
-  issn        = {2824-7795}
+  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.}
 }