83 citations · 103 across the 11 of their papers we have counts for
12 papers
Gold standard process Markovian poisoning: a semiparametric approach
Claire Lacour, Pierre Vandekerkhove
We consider in this paper a stochastic process that mixes in time, according to a nonobserved stationary Markov selection process, two separate sources of randomness: i) a stationa…
Markov Kernels Local Aggregation for Noise Vanishing Distribution Sampling
Florian Maire, Pierre Vandekerkhove
A novel strategy that combines a given collection of -reversible Markov kernels is proposed. At each Markov transition, one of the available kernels is selected via a state-depe…
Semiparametric topographical mixture models with symmetric errors
Cristina Butucea, Rodrigue Ngueyep Tzoumpe, Pierre Vandekerkhove
Motivated by the analysis of a Positron Emission Tomography (PET) imaging data considered in Bowen et al. (2012), we introduce a semiparametric topographical mixture model able to…
Semiparametric estimation of a two-component mixture of linear regressions in which one component is known
L. Bordes, I. Kojadinovic, P. Vandekerkhove
A new estimation method for the two-component mixture model introduced in \cite{Van13} is proposed. This model consists of a two-component mixture of linear regressions in which on…
Estimation of a semiparametric contaminated regression model
Pierre Vandekerkhove
We consider in this paper a contamined regression model where the distribution of the contaminating component is known when the Eu- clidean parameters of the regression model, the…
Semiparametric mixtures of symmetric distributions
Cristina Butucea, Pierre Vandekerkhove
We consider in this paper the semiparametric mixture of two distributions equal up to a shift parameter. The model is said to be semiparametric in the sense that the mixed distribu…
A simple variance inequality for U-statistics of a Markov chain with applications
Gersende Fort, Eric Moulines, Pierre Priouret +1
We establish a simple variance inequality for U-statistics whose underlying sequence of random variables is an ergodic Markov Chain. The constants in this inequality are explicit a…
A central limit theorem for adaptive and interacting Markov chains
Gersende Fort, Eric Moulines, Pierre Priouret +1
Adaptive and interacting Markov Chains Monte Carlo (MCMC) algorithms are a novel class of non-Markovian algorithms aimed at improving the simulation efficiency for complicated targ…
The Logarithmic Sobolev Constant of The Lamplighter
Evgeny Abakumov, Anne Beaulieu, François Blanchard +17
We give estimates on the logarithmic Sobolev constant of some finite lamplighter graphs in terms of the spectral gap of the underlying base. Also, we give examples of application.
On ergodic two-armed bandits
Pierre Tarrès, Pierre Vandekerkhove
A device has two arms with unknown deterministic payoffs and the aim is to asymptotically identify the best one without spending too much time on the other. The Narendra algorithm…
Semiparametric estimation of a two-component mixture model
Laurent Bordes, Stéphane Mottelet, Pierre Vandekerkhove
Suppose that univariate data are drawn from a mixture of two distributions that are equal up to a shift parameter. Such a model is known to be nonidentifiable from a nonparametric…
Selection of a MCMC simulation strategy via an entropy convergence criterion
Didier Chauveau, Pierre Vandekerkhove
In MCMC methods, such as the Metropolis-Hastings (MH) algorithm, the Gibbs sampler, or recent adaptive methods, many different strategies can be proposed, often associated in pract…