238 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Nantes UniversitéFR266 papers
- Centre National de la Recherche ScientifiqueFR187 papers
- Université de NantesFR152 papers
- École Centrale de NantesFR48 papers
- Laboratoire de Mathématiques d'OrsayFR36 papers
- Institut de recherche mathématique de RennesFR25 papers
- Institut FourierFR25 papers
- Université Paris-SudFR25 papers
- Université Paris-SaclayFR17 papers
- Institut national de recherche en sciences et technologies du numériqueFR12 papers
- Analyse, Géométrie et ModélisationFR11 papers
- Institut de Mathématiques de BordeauxFR11 papers
39 papers · 1 filter
Parametric estimation and LAN property of the birth-death-move process with mutations
Lisa Balsollier, Frédéric Lavancier
A birth-death-move process with mutations is a Markov model for a system of marked particles in interaction, that move over time, with births and deaths. In addition the mark of ea…
Learning with tree tensor networks: complexity estimates and model selection
Bertrand Michel, Anthony Nouy
Tree tensor networks, or tree-based tensor formats, are prominent model classes for the approximation of high-dimensional functions in computational and data science. They correspo…
Shadow Simulated Annealing algorithm: a new tool for global optimisation and statistical inference
R. Stoica, Madalina Deaconu, Anne Philippe +1
This paper develops a new global optimisation method that applies to a family of criteria that are not entirely known. This family includes the criteria obtained from the class of…
Robust Bregman Clustering
Aurélie Fischer, Clément Levrard, Claire Brécheteau
Using a trimming approach, we investigate a k-means type method based on Bregman divergences for clustering data possibly corrupted with clutter noise. The main interest of Bregman…
Asymptotic distribution of least square estimators for linear models with dependent errors
Emmanuel Caron
In this paper, we consider the usual linear regression model in the case where the error process is assumed strictly stationary. We use a result from Hannan (1973), who proved a Ce…
Averaging of density kernel estimators
O. Chernova, F. Lavancier, P. Rochet
Averaging provides an alternative to bandwidth selection for density kernel estimation. We propose a procedure to combine linearly several kernel estimators of a density obtained f…
Estimating long memory in panel random-coefficient AR(1) data
Remigijus Leipus, Anne Philippe, Vytaute Pilipauskaite +1
It is well-known that random-coefficient AR(1) process can have long memory depending on the index of the tail distribution function of the random coefficient, if it is a regul…
Asymptotic distribution of least squares estimators for linear models with dependent errors : regular designs
Emmanuel Caron, Sophie Dede
In this paper, we consider the usual linear regression model in the case where the error process is assumed strictly stationary. We use a result from Hannan, who proved a Central L…
A tutorial on estimator averaging in spatial point process models
Frédéric Lavancier, P Rochet
Assume that several competing methods are available to estimate a parameter in a given statistical model. The aim of estimator averaging is to provide a new estimator, built as a l…
The Mean/Max Statistic in Extreme Value Analysis
Paul Rochet, Isabel Serra
Most extreme events in real life can be faithfully modeled as random realizations from a Generalized Pareto distribution, which depends on two parameters: the scale and the shape.…
Standard and robust intensity parameter estimation for stationary determinantal point processes
Jean-François Coeurjolly, Christophe Ange Napoléon Biscio
This work is concerned with the estimation of the intensity parameter of a stationary determinantal point process. We consider the standard estimator, corresponding to the number o…
Reconstructing undirected graphs from eigenspaces
Yohann De Castro, Thibault Espinasse, Paul Rochet
In this paper, we aim at recovering an undirected weighted graph of vertices from the knowledge of a perturbed version of the eigenspaces of its adjacency matrix . For insta…
Contrast estimation for parametric stationary determinantal point processes
Christophe Biscio, Frédéric Lavancier
We study minimum contrast estimation for parametric stationary determi-nantal point processes. These processes form a useful class of models for repulsive (or regular, or inhibitiv…
Nonparametric estimation of the distribution of the autoregressive coefficient from panel random-coefficient AR(1) data
Remigijus Leipus, Anne Philippe, Vytautė Pilipauskaitė +1
We discuss nonparametric estimation of the distribution function of the autoregressive coefficient from a panel of random-coefficient AR(1) data, each of…
ABC Shadow algorithm: a tool for statistical analysis of spatial patterns
R. S. Stoica, A. Philippe, P. Gregori +1
This paper presents an original ABC algorithm, "ABC Shadow", that can be applied to sample posterior densities that are continuously differentiable. The proposed method uses the id…
Consistency of likelihood estimation for Gibbs point processes
David Dereudre, Frédéric Lavancier
Strong consistency of the maximum likelihood estimator (MLE) for parametric Gibbs point process models is established. The setting is very general. It includes pairwise pair potent…
Hypothesis testing for markovian models with random time observations
Flavia Barsotti, Anne Philippe, Paul Rochet
The aim of this paper is to propose a methodology for testing general hypothesis in a Markovian setting with random sampling. A discrete Markov chain X is observed at random time i…
Quantifying repulsiveness of determinantal point processes
Christophe Ange Napoléon Biscio, Frédéric Lavancier
Determinantal point processes (DPPs) have recently proved to be a useful class of models in several areas of statistics, including spatial statistics, statistical learning and tele…
Estimating the transition matrix of a Markov chain observed at random times
Flavia Barsotti, Yohann De Castro, Thibault Espinasse +1
In this paper we develop a statistical estimation technique to recover the transition kernel of a Markov chain in presence of censored data. We cons…
Aggregation and long memory: recent developments
Remigijus Leipus, Anne Philippe, Donata Puplinskaite +1
It is well-known that the aggregated time series might have very different properties from those of the individual series, in particular, long memory. At the present time, aggregat…
Contemporaneous aggregation of triangular array of random-coefficient AR(1) processes
Anne Philippe, Donata Puplinskaite, Donatas Surgailis
We discuss contemporaneous aggregation of independent copies of a triangular array of random-coefficient AR(1) processes with i.i.d. innovations belonging to the domain of attracti…
Estimation of Scale and Hurst Parameters of Semi-Selfsimilar Processes
Saeid Rezakhah, Anne Philippe, Navideh Modarresi
The characteristic feature of semi-selfsimilar process is the invariance of its finite dimensional distributions by certain dilation for specific scaling factor. Estimating the sca…
Estimation of the intensity parameter of the germ-grain Quermass-interaction model when the number of germs is not observed
David Dereudre, Frédéric Lavancier, Katerina Helisova Stankova
The Quermass-interaction model allows to generalise the classical germ-grain Boolean model in adding a morphological interaction between the grains. It enables to model random stru…
Determinantal point process models and statistical inference : Extended version
Frédéric Lavancier, Jesper Møller, Ege Rubak
Statistical models and methods for determinantal point processes (DPPs) seem largely unexplored. We demonstrate that DPPs provide useful models for the description of spatial point…