95 citations · 256 across the 23 of their papers we have counts for
14 papers · 1 filter
Copula-like inference for discrete bivariate distributions with rectangular supports
Ivan Kojadinovic, Tommaso Martini
After reviewing a large body of literature on the modeling of bivariate discrete distributions with finite support, \cite{Gee20} made a compelling case for the use of -projectio…
Multi-purpose open-end monitoring procedures for multivariate observations based on the empirical distribution function
Mark Holmes, Ivan Kojadinovic, Alex Verhoijsen
We propose nonparametric open-end sequential testing procedures that can detect all types of changes in the contemporary distribution function of possibly multivariate observations…
Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions
Ivan Kojadinovic, Ghislain Verdier
The aim of sequential change-point detection is to issue an alarm when it is thought that certain probabilistic properties of the monitored observations have changed. This work is…
Combining cumulative sum change-point detection tests for assessing the stationarity of univariate time series
Axel Bücher, Jean-David Fermanian, Ivan Kojadinovic
We derive tests of stationarity for univariate time series by combining change-point tests sensitive to changes in the contemporary distribution with tests sensitive to changes in…
Some copula inference procedures adapted to the presence of ties
Ivan Kojadinovic
When modeling the distribution of a multivariate continuous random vector using the so-called \emph{copula approach}, it is not uncommon to have ties in the coordinate samples of t…
Detecting distributional changes in samples of independent block maxima using probability weighted moments
Ivan Kojadinovic, Philippe Naveau
The analysis of seasonal or annual block maxima is of interest in fields such as hydrology, climatology or meteorology. In connection with the celebrated method of block maxima, we…