5 citations · 9 across the 7 of their papers we have counts for
7 papers
Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series
Liudmila Pishchagina, Guillem Rigaill, Vincent Runge
We consider the problem of detecting multiple changes in multiple independent time series. The search for the best segmentation can be expressed as a minimization problem over a gi…
Exponential Integral Solutions for Fixation Time in Wright-Fisher Model With Selection
Vincent Runge, Arnaud Liehrmann, Pauline Spinga
In this work we derive new analytic expressions for fixation time in Wright-Fisher model with selection. The three standard cases for fixation are considered: fixation to zero, to…
Change-in-Slope Optimal Partitioning Algorithm in a Finite-Size Parameter Space
Vincent Runge, Marco Pascucci, Nicolas Deschamps de Boishebert
We consider the problem of detecting change-points in univariate time series by fitting a continuous piecewise linear signal using the residual sum of squares. Values of the inferr…
Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise
Gaetano Romano, Guillem Rigaill, Vincent Runge +1
Whilst there are a plethora of algorithms for detecting changes in mean in univariate time-series, almost all struggle in real applications where there is autocorrelated noise or w…
gfpop: an R Package for Univariate Graph-Constrained Change-Point Detection
Vincent Runge, Toby Dylan Hocking, Gaetano Romano +3
In a world with data that change rapidly and abruptly, it is important to detect those changes accurately. In this paper we describe an R package implementing a generalized version…
Is a Finite Intersection of Balls Covered by a Finite Union of Balls in Euclidean Spaces ?
Vincent Runge
Considering a finite intersection of balls and a finite union of other balls in an Euclidean space, we propose an exact method to test whether the intersection is covered by the un…
On the Limit Imbalanced Logistic Regression by Binary Predictors
Vincent Runge
In this work, we introduce a modified (rescaled) likelihood for imbalanced logistic regression. This new approach makes easier the use of exponential priors and the computation of…