Publications

Publications

  1. Adaptive estimation for stochastic damping hamiltonian systems under partial observation, Comte F, Prieur C, Samson A, Stochastic Processes and Their Applications, to appear
  2. Spline regression for hazard rate estimation when data are censored and measured with error, Comte F, Mabon G, Samson A, Statistica Neerlandica, 71, 115-140, 2017.
  3. Analysis of Temozolomide resistance in low-grade gliomas by using a mechanistic mathematical model Ollier E, Mazzocco P, Ricard D, Kaloshi G, Idbaih A, Alentorn A, Psimaras D, Honnorat J, Delattre JY, Grenier E, Ducray F, Samson, A, Fundamental & Clinical Pharmacology, to appear
  4. Unified approach for extrapolation and bridging of adult information in early-phase dose-finding paediatric studies  Petit C, Samson A, Morita S, Ursino M, Guedj J, Jullien V, Comets E, Zohar S, Statistical Methods in Medical Research, to appear
  5. A comparative study of the precision of Carstens and NDI electromagnetic articulographs Savariaux C, Badin P, Samson A, Gerber S, Journal of Speech, Language, and Hearing Research, 60, 322–340, 2017.
  6. Parametric estimation of complex mixed models based on meta-model approach, Barbillon P, Barthelemy C, Samson A, Statistics and Computing, to appear. 
  7. Population parametrization of costly black box models using iterations between SAEM algorithm and kriging,Grenier E, Helbert C, Louvet V, Samson A, Vigneaux P, Computational and Applied Mathematics, 2016.
  8. Physiological and acoustic characteristics of the male music theatre voice Bourne T, Garnier M, Samson A, Journal of Acoustical Society of America, 140, 610, 2016. 
  9. Mixtures of stochastic differential equations with  random effects: application to  data clustering,Delattre M, Genon-Catalot V, Samson A, Journal of Statistical Planning and Inference, 173:109-124, 2016.
  10. A SAEM Algorithm for Fused Lasso Penalized Non Linear Mixed Effect Models: Application to Group Comparison in Pharmacokinetic  Ollier E, Samson A, Delavenne X, Viallon V, Computational Statistics and Data Analysis, 96:207-221, 2016.
  11. Designing a paediatric study for an antimalarial drug including prior information from adults Petit C, Jullien V, Samson A, Guedj J, Kiechel JR, Zohar S, Comets E, Antimicrobial Agents and Chemotherapy, 60:1481-1491, 2016.
  12. Parameter estimation in neuronal stochastic differential equation models from intracellular recordings of membrane potentials in single neurons: a Review Ditlevsen S, Samson A, Journal de la SFdS, 157:6-21, 2016.
  13. Estimation of population parameters in stochastic differential equations with random effects in the diffusion coefficient Delattre M, Genon-Catalot V, Samson A, ESAIM P&S, 19, 2015.
  14. Estimation in the partially observed stochastic Morris-Lecar neuronal model with particle filter and stochastic approximation methods. Ditlevsen S, Samson A, Annals of Applied Statistics, 2, 674-702, 2014.
  15. Using PMCMC in EM algorithm for stochastic mixed models: theoretical and practical issues, Donnet S, Samson A, Journal de la Société Francaise de Statistique, 155, 49-72, 2014.
  16. Deconvolution estimation of onset of pregnancy with replicate observations. Comte F, Samson A, Stirnemann J, Scandinavian Journal of Statistics, 41, 325-345, 2014.
  17. Estimation in autoregressive model with measurement error. Dedecker J, Samson A, Taupin ML. ESAIM P&S18 227-307, 2014.
  18. Routine OGTT: a robust model including incretin effect for precise identification of insulin sensitivity and secretion in a single individual. De Gaetano A, Panunzi S, Matone A, Samson A, Kautzky-Willer A, Vrbikova J, Bendlová B, Pacin G.,PLOS ONE, 8(8): e70875.
  19. Nonparametric estimation for stochastic differential equations with random effects. Comte F, Genon-Catalot V, Samson A. Stochastic Processes and Their Applications,  123(7): 2522–2551, 2013
  20. Day-specific probabilities of conception in spontaneous pregnancies. Stirnemann J, Samson A, Bernard JP, Thalabard JC. Human Reproduction, 28(4):1110-1116, 2013.
  21. Maximum likelihood estimation for stochastic differential equations with random effects. Delattre M, Genon-Catalot V, Samson A,  Scandinavian Journal of Statistics, 40(2): 322-343, 2013
  22. A review on estimation of stochastic differential equations for pharmacokinetic - pharmacodynamic models . Donnet S, Samson A, Advanced Drug Delivery Reviews, 65(7): 929–939, 2013
  23. Density estimation of a biomedical variable subject to measurement error using an auxiliary set of replicate observations. Stirnemann J, Comte F, Samson A, Statistics in Medicine, 31(30): 4154-4163, 2012.
  24. Nonparametric estimation of random effects densities in linear mixed-effects model. Comte F, Samson A, Journal of Nonparametric Statistics, 24: 951-975, 2012.
  25. Contrast estimator for  completely or partially observed hypoelliptic diffusion. Samson A, Thieullen M, Stochastic Processes and Their Applications, 122:2521-2552, 2012
  26. Multiple treatment comparisons in a series of anti-malarials trials with an ordinal primary outcome and repeated treatment evaluations. Whegang SY, Samson A, Basco L, Thalabard JC, Malaria Journal, 2012, 3;11(1):147.
  27. Individual predictions based on population nonlinear mixed modeling: application to prenatal twin growth. Stirnemann J, Samson A, Thalabard JC, Statistics in Medicine, 2012, 31:1986-1999.
  28. Parameter estimation and change-point detection from Dynamic Contrast Enhanced MRI data using stochastic differential equations. Cuenod CA, Favetto B, Genon-Catalot V,  Rozenholc Y, Samson A,  Mathematical Biosciences, 233(1):68-76, 2011.
  29. Maximum likelihood estimation of long term HIV dynamic models and antiviral response. Lavielle M, Samson A, Fermin AK, Mentre F, Biometrics, 67(1):250–259, 2011
  30. Bayesian analysis of growth curves using mixed models defined by stochastic differential equations. Donnet S, Foulley JL, Samson A,  Biometrics, 66(3):733–741, 2010
  31. Parameter estimation for a bidimensional partially observed Ornstein-Uhlenbeck process with biological application. Favetto B, Samson A, Scandinavian Journal of Statistics, 37(2):200-220, 2010
  32. Phenomenological modeling of tumor diameter growth based on a mixed effects model. Bastogne T, Samson A, Vallois P, Wantz-Mézières S, Pinel S, Bechet D, Barberi-Heyob M, Journal of Theoretical Biology, 262:544-552, 2010.
  33. A SAEM algorithm for the estimation of template and deformation parameters in medical image sequences. Richard F, Samson A, Cuenod CA, Statistics and Computing, 19:465-478, 2009.
  34. Extension of the SAEM algorithm for the nonlinear mixed models with two levels of random effects. Panhard X, Samson A, Biostatistics,  10:121-35, 2009
  35. Parametric inference for mixed models defined by stochastic differential equations. Donnet S, Samson A, ESAIM P&S, 12:196-218, 2008.
  36. Missing data in randomized controlled trials of rheumatoid arthritis with radiographic outcomes: a simulation study. Baron G, Ravaud P, Samson A, Giraudeau B, Arthritis Care & Research, 59(1):25-31, 2008.
  37. Design in nonlinear mixed effects models: optimization using the Fedorov-Wynn algorithm and power of the Wald test for binary covariates. Retout S, Comets E, Samson A, Mentré F, Statistics in Medicine, 26(28):5162-5179, 2007.
  38. Estimation of parameters in incomplete data models defined by dynamical systems. Donnet S, Samson A, Journal of Statistical Planning and Inference, 137(9):2815-31, 2007.
  39. The SAEM algorithm for group comparison tests in longitudinal data analysis based on non-linear mixed-effects model. Samson A, Lavielle M, Mentré F, Statistics in Medicine, 26(27):4860-4875, 2007.
  40. Discussion on “Parameter estimation for differential equations: a generalized smoothing approach” (by Ramsay JO, Hooker G, Campbell D and Cao J), Donnet S, Samson A, Journal of the Royal Statistical Society: Series B, 69(5):741-796, 2007
  41. Extension of the SAEM algorithm to left-censored data in non-linear mixed-effects model: application to HIV dynamics model. Samson A, Lavielle M, Mentré F, Computational Statistics and Data Analysis, 51(3):1562-74, 2006.

Book chapter

  1.  Introduction to stochastic models in biology.   Ditlevsen S, Samson A.  In Bachar, Batzel and Ditlevsen (Eds.), Stochastic Methods and Neuron Modeling. Springer. 2012

Preprint

  1. Stochastic Proximal Gradient Algorithms for Penalized Mixed Models, Fort G, Ollier E, Samson A, submitted
  2. Statistical methodology for the analysis of repeated duration data in speech, language and hearing research, Letué F, Martinez MJ, Samson A, Vilain A, Vilain C, submitted
  3. Hypoelliptic stochastic FitzHugh-Nagumo neuronal model: mixing, up-crossing and estimation of the spike rate, Leòn JR, Samson A, submitted
  4. Coupling stochastic EM and Approximate Bayesian Computation for parameter inference in state-space models, Picchini U, Samson A, submitted
  5. Hazard estimation for censored data contaminated with additive measurement error: Application to length of pregnancy, Comte F, Samson A, Stirnemann J, submitted.
  6. Mixedsde: an R package to fit mixed stochastic differential equations, Dion C, Hermann S, Samson A, submitted.
  7. Multivariate inhomogeneous diffusion models with covariates and mixed effects, Grosse Ruse M, Samson A, Ditlevsen S, submitted.
  8. EM algorithm coupled with particle filter for maximum likelihood parameter estimation of stochastic differential mixed-effects models . Donnet S, Samson A. Prepublication MAP5 2010-24

Publications in Proceedings

  1. System identification of tumor growth described by a mixed effects model. Bastogne T, Samson A, Mézières-Wantz S, Vallois P, Pinel S, Barberi-Heyob M, Proceedings of IFAC Symposium on system identification, 2009.
  2. Metropolis-Hasting techniques for finite element-based registration . Richard F, Samson A, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.

Copyright © Adeline Samson 2007