Nonparametric estimation of the conditional distribution of the inter-jumping times for piecewise-deterministic Markov processes

This paper presents a nonparametric method for estimating the conditional density associated to the jump rate of a piecewise-deterministic Markov process. In our framework, the estimation needs only one observation of the process within a long time interval. Our method relies on a generalization of Aalen's multiplicative intensity model. We prove the uniform consistency of our estimator, under some reasonable assumptions related to the primitive characteristics of the process. A simulation example illustrates the behavior of our estimator

Random coefficients bifurcating autoregressive processes

This paper presents a model of asymmetric bifurcating autoregressive process with random coefficients. We couple this model with a Galton Watson tree to take into account possibly missing observations. We propose least-squares estimators for the various parameters of the model and prove their consistency with a convergence rate, and their asymptotic normality. We use both the bifurcating Markov chain and martingale approaches and derive new important general results in both these frameworks

Asymmetry tests for Bifurcating Auto-Regressive Processes with missing data

We present symmetry tests for bifurcating autoregressive processes (BAR) when some data are missing. BAR processes typically model cell division data. Each cell can be of one of two types \emph{odd} or \emph{even}. The goal of this paper is to study the possible asymmetry between odd and even cells in a single observed lineage. We first derive asymmetry tests for the lineage itself, modeled by a two-type Galton-Watson process, and then derive tests for the observed BAR process. We present applications on both simulated and real data

A general definition of influence between stochastic processes

We extend the study of weak local conditional independence (WCLI) based on a measurability condition made by Commenges and G\'egout-Petit (2009) to a larger class of processes that we call D'. We also give a definition related to the same concept based on certain likelihood processes, using the Girsanov theorem. Under certain conditions, the two definitions coincide on D'. These results may be used in causal models in that we define what may be the largest class of processes in which influences of one component of a stochastic process on another can be described without ambiguity. From WCLI we can contruct a concept of strong local conditional independence (SCLI). When WCLI does not hold, there is a direct influence while when SCLI does not hold there is direct or indirect influence. We investigate whether WCLI and SCLI can be defined via conventional independence conditions and find that this is the case for the latter but not for the former. Finally we recall that causal interpretation does not follow from mere mathematical definitions, but requires working with a good system and with the true probability

Modèles probabilistes pour l'initiation et la propagation de fissures

National audienceL'étude de la dégradation et du vieillissement des structures ou des matériels embarqués nécessite la modélisation de phénomènes mécaniques de fatigue. Cette modélisation conditionne les calculs de durées de vie et de probabilités d'occurrence d'événements redoutés et est donc particulièrement importante en ce qui concerne la sécurité. Même soumis à des régimes de fatigue contrôlés, les phénomènes d'initiation ou de propagation de fissures présentent un caractère aléatoire

Package 'armada' : A Statistical Methodology to Select Covariates in High-Dimensional Data under Dependence

An R package, available on the CRAN. A Statistical Methodology to Select Covariates in High-Dimensional Data under Dependenc

Network inference for truncated gaussian data

International audienc