activity
20192025
most citedStatistical visualisation for tidy and geospatial data in R via kernel smoothing methods in the eks package

10 citations · 10 across the 2 of their papers we have counts for

collaborators

7 papers

stat.CO2025

Interpretable contour level selection for heat maps for gridded data

Tarn Duong

Gridded data formats, where the observed multivariate data are aggregated into grid cells, ensure confidentiality and reduce storage requirements, with the trade-off that access to…

stat.CO2022★ 10 cited

Statistical visualisation for tidy and geospatial data in R via kernel smoothing methods in the eks package

Tarn Duong

Kernel smoothers are essential tools for data analysis due to their ability to convey complex statistical information with concise graphical visualisations. Their inclusion in the…

stat.AP2021

Relaxing door-to-door matching reduces passenger waiting times: a workflow for the analysis of driver GPS traces in a stochastic carpooling service

Panayotis Papoutsis, Safa Fennia, Constant Bridon +1

Carpooling has the potential to transform itself into a mass transportation mode by abandoning its adherence to deterministic passenger-driver matching for door-to-door journeys, a…

math.CA2020

Higher order differential analysis with vectorized derivatives

José E. Chacón, Tarn Duong

Higher order derivatives of functions are structured high dimensional objects which lend themselves to many alternative representations, with the most popular being multi-index, ma…

stat.AP2020

Bayesian hierarchical models for the prediction of the driver flow and passenger waiting times in a stochastic carpooling service

Panayotis Papoutsis, Bertrand Michel, Anne Philippe +1

Carpooling is an integral component in smart carbon-neutral cities, in particular to facilitate homework commuting. We study an innovative carpooling service developed by the start…

cs.LG2019

Nearest Neighbor Median Shift Clustering for Binary Data

Gaël Beck, Tarn Duong, Mustapha Lebbah +1

We describe in this paper the theory and practice behind a new modal clustering method for binary data. Our approach (BinNNMS) is based on the nearest neighbor median shift. The me…