Books

  1. Book: R. Douc, E. Moulines and D. Stoffer: Non Linear Time series: Theory, Methods and Applications with R Examples. Wiley Edition.
  2. Book: R. Douc, E. Moulines, P. Priouret and P. Soulier: Markov chains. Springer Edition.

All (or maybe almost all) the papers I have written may be downloaded at Arxiv preprint service or Hal archives-ouvertes.

Journal papers

For getting access to the website using the DOI number, please go to http://dx.doi.org/DOI-number
AuteursTitreJournalAnnéeRéférences
Liens
K. Daudel, R. Douc, F. RoueffMonotonic alpha-divergence minimisationSubmitted (2021)
M. Gerber, R. DoucOnline Approximate Bayesian learningSubmitted (2020)
K. Daudel, R. Douc, F. PortierInfinite-dimensional gradient-based descent for alpha-divergence minimisationAnnals of Statistics2020. To be published.
R. Douc, F. Roueff and T. SimNecessary and sufficient conditions for the identifiability of observation-driven modelsJournal of Time Series Analysis (JTSA)2020 (sept)Volume 42, Issue 2, p140-160
R. Douc, J. Olsson and F. RoueffPosterior consistency for partially observed Markov modelsStochastic Processes and their applications2020Volume 130, Issue 2, february 2020, Pages 733-759
R. Douc and J. OlssonNumerically stable online estimation of variance in particle filtersBernoulli2019Volume 25, Number 2, 1504-1535.
R. Douc, K. Fokianos and E. MoulinesAsymptotic properties of Quasi-Maximum Likelihood
Estimators in Observation-Driven Time Series models
Electronic Journal of Statistics2017Volume 11, Number 2 (2017), 2707-2740.
R. Douc, F. Roueff and T. SimThe maximizing set of the asymptotic normalized log-likelihood for partially observed Markov chainsAnnals of Applied probability2016Volume 26, Number 4, 2357-2383.
R. Douc, F. Lindsten and E. MoulinesUniform ergodicity of the Particle Gibbs samplerScandinavian Journal of Statistics2015 (sept.)
Volume 42, Issue 3, pp 775-797
R. Douc, F. Roueff and T. SimHandy sufficient conditions for the convergence of the maximum
likelihood estimator in observation-driven models
Lituanian Mathematical Journal2015Volume 55, Issue 3, pp 367-392
R. Douc, J. Olsson and F. MaireOn the use of Markov chain Monte Carlo methods for the sampling of mixture modelsStatistics and Computing2015Volume 25, Issue 1, pp 95-110.
F. Maire, R. Douc, J. OlssonComparison of Asymptotic Variances of Inhomogeneous Markov Chains with Applications to Markov Chain Monte Carlo MethodsAnnals of Statistics2014Volume 42, No. 4, p 1483–1510
R. Douc, E. Moulines and J. OlssonLong-term stability of sequential Monte Carlo methods under verifiable conditionsAnnals of Applied Probability2014Volume 24, No. 5, p 1767–1802
C. Dubarry, R. DoucCalibrating the exponential Ornstein-Uhlenbeck multiscale stochastic volatility modelQuantitative finance2014Volume 14, Issue 3, p 443-456.DOI: 10.1080/14697688.2012.738929
M. Bédard, R. Douc and E. MoulinesScaling analysis of Delayed Rejection MCMC methodsMethodology and Computing in Applied
Probability
2014Volume 16, Issue 4, P 811-838. DOI: 10.1007/s11009-013-9326-y
R. Douc, P. Doukhan and E. MoulinesErgodicity of observation-driven time series models and
consistency of the maximum likelihood estimator
Stochastic Processes and their Applications2013Volume 123, Issue 7, July 2013, Pages 2620-2647DOI: 10.1016/j.spa.2013.04.010
R. Douc and E. MoulinesAsymptotic properties of the maximum likelihood estimation in misspecified Hidden Markov modelsAnnals of Statistics2012Oct. 2012, Volume 40, Number 5 (2012), 2697-2732.
M. Bédard, R. Douc and E. Moulines Scaling analysis of multiple-try MCMC methodsStochastic Processes and their Applications2012Volume 122, Issue 3, Pages 758-786
R. Douc, A. Garivier, E. Moulines, J. Olsson.Sequential Monte Carlo smoothing for general state space hidden Markov modelsAnnals of Applied Probability2011Volume 21, Number 6 (2011), 2109-2145
R. Douc, C.P. RobertA vanilla Rao-Blackwellisation of Metropolis Hastings algorithmsAnnals of Statistics2011Volume 39, Number 1, 261-277.
R. Douc, E. Moulines, J. Olsson and R. Van HandelConsistency of the maximum likelihood estimator for general hidden Markov modelsAnnals of Statistics2011Volume 39, Number 1, 474-513.
R. Douc, E. Gassiat, B. Landelle, E. MoulinesForgetting the initial distribution in non-ergodic
Hidden Markov models
Annals of Applied Probability2010 Volume 20, Number 5, 1638-1662.
R. Douc, E. Moulines and Y. Ritov.
Forgetting of the initial
condition for the filter in general state-space hidden Markov chain: a
coupling approach


Electronic Journal of Probability 2009Volume 14, pp 27-49.
R. Douc, G. Fort and A. Guillin.
Subgeometric rates of convergence of f-ergodic
strong Markov processes
Stochastic Processes and their Applications. 2009Volume 119 Number
3, 897-923.
R.Douc, E. Moulines and J. OlssonOptimality of the auxillary
particle filter
Probability and Mathematical Statistics.2009Volume 29, issue 1, pages 1-28
R. Douc, G. Fort, E. Moulines and P. Priouret.

Forgetting of the initial distribution for Hidden
Markov Models
Stochastic Processes and their Applications, 2009Volume 119, Number 4, Pages
1235-1256
R. Douc, F. Roueff and P. Soulier. On the existence of ARCH(infini) processes



Stochastic Processes and their Applications. 2008Volume 118, Issue
5, 755-761.
R.Douc and E. Moulines
Limit theorems for weighted samples with applications
to Sequential Monte Carlo Methods


Annals of Statistics 2008Volume 36, Number 5 , 2344-2376
R. Douc, A. Guillin and E. Moulines. Bounds on regeneration times and limit theorems for
subgeometric Markov chains



Annales de l'I.H.P 2008Volume 44, Number 2, 239-257.
O. Cappé, R. Douc, E. Moulines and J. Olsson.Sequential Monte Carlo
smoothing with application to parameter estimation in non linear state
space models



Bernoulli. 2008Volume 14, Number 1 , 155-179.
O. Cappé, R. Douc, A. Guillin, J. M. Marin and C. Robert.
Adaptive importance sampling
in general mixture classes


Statistics and Computing 2008Volume 18, Number 4, 447-459
R. Douc, A. Guillin, J.M. Marin, C.P. Robert.
Convergence of adaptive mixtures of importance
sampling schemes


Annals of Statistics 2007Volume 35, Number 1.
R. Douc, A. Guillin, J.M. Marin, C.P. Robert. Minimum variance importance sampling via
Population Monte Carlo


Esaim P&S. 2007 Volume 11, pp. 427-447
R. Douc, E. Moulines and P. Soulier.
Computable bounds for subgeometric ergodic Markov
chains


Bernoulli 2007Volume 13, Number 3 , 831-848.
R. Douc, A. Guillin and J. Najim.

Moderate
deviation in particle filtering

Annals of Applied Probability 2005Volume 15 , no. 1B, 587-614
R. Douc, E. Moulines, T. Ryden.
Asymptotic
properties of the maximum likelihood estimator in autoregressive models
with Markov regime


Annals of Statistics2004Volume 32 , no. 5, 2254-2304
R. Douc, G. Fort, E. Moulines, P. Soulier.
Practical drift conditions for subgeometric rates of convergence


Annals of Applied Probability 2004Volume 14, no. 3, 1353--1377. 60J10
R.Douc, E. Moulines, J. Rosenthal.
Quantitative
bounds for geometric convergence rates of Markov chains


Annals of Applied Probability 2004Volume 14, no. 4, 1643-1665.
O.Cappé, R.Douc, E.Moulines, C.Robert.

On the Convergence of the Monte-Carlo Maximum Likelihood for Latent
Variable Models

Scandinavian Journal of Statistics 2002Volume 29 issue 4, p. 615-635,
R.Douc, C.Matias.
Asymptotics of the Maximum Likelihood Estimator for general Hidden
Markov Models


Bernoulli, 2001Volume 7, no. 3, 381--420, .
R.Douc, C.Matias. Propriétés asymptotiques de l'estimateur de maximum de vraisemblance
pour des modèles de Markov cachés généraux



C.R Académie des Sciences2000Série I, p.135-138