CQFD - 2018 - Annual activity report

CQFD

CQFD - 2018

Project-Team Cqfd

Team, Visitors, External Collaborators

Overall Objectives

Presentation

Research Program

Application Domains

Dependability and safety

New Software and Platforms

New Results

A new characterization of the jump rate for piecewise-deterministic Markov processes with discrete transitions
Estimation of the average number of continuous crossings for non-stationary non-diffusion processes
ClustGeo: an R package for hierarchical clustering with spatial constraints
Change-point detection for Piecewise Deterministic Markov Processes
A sharp first order analysis of Feynman–Kac particle models, Part I: Propagation of chaos
A sharp first order analysis of Feynman–Kac particle models, Part II: Particle Gibbs samplers
Exponential mixing properties for time inhomogeneous diffusion processes with killing
Investigation of asymmetry in E. coli growth rate
Design of estimators for restoration of images degraded by haze using genetic programming
Controlling IL-7 injections in HIV-infected patients
Stochastic Control of Observer Trajectories in Passive Tracking with Acoustic Signal Propagation Optimization
Computable approximations for average Markov decision processes in continuous time
Zero-Sum Discounted Reward Criterion Games for Piecewise Deterministic Markov Processes
Approximation of discounted minimax Markov control problems and zero-sum Markov games using Hausdorff and Wasserstein distances
On the expected total cost with unbounded returns for Markov decision processes
Applying Genetic Improvement to a Genetic Programming library in C++

Bilateral Contracts and Grants with Industry

Bilateral Contracts with Industry

Partnerships and Cooperations

Dissemination

Bibliography

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Section: New Software and Platforms

QuantifQuantile

Keyword: Regression

Functional Description: QuantifQuantile is an R package that allows to perform quantization-based quantile regression. The different functions of the package allow the user to construct an optimal grid of N quantizers and to estimate conditional quantiles. This estimation requires a data driven selection of the size N of the grid that is implemented in the functions. Illustration of the selection of N is available, and graphical output of the resulting estimated curves or surfaces (depending on the dimension of the covariate) is directly provided via the plot function.

Contact: Jérôme Saracco
URL: https://cran.r-project.org/web/packages/QuantifQuantile/index.html

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