Les modÃ¨les non linÃ©aires Ã effets mixtes - Isped

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Convergence of EM Dempster et al. (1977), Wu (1983) Convergence of (θ k ) to a stationary point ̂θ g of the observed likelihood is ensured under some regularity conditions. Sminaire ”Statistique et Sant Publique”, Bordeaux - 8 novembre 2005, p.24
Convergence of EM Dempster et al. (1977), Wu (1983) Convergence of (θ k ) to a stationary point ̂θ g of the observed likelihood is ensured under some regularity conditions. Some practical drawbacks of EM: Nature of the limiting point. Convergence depends on the initial guess. Slow convergence of EM. Sminaire ”Statistique et Sant Publique”, Bordeaux - 8 novembre 2005, p.24
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Les modèles non linéaires à effe
Page 3 and 4:
A pharmacokinetics example : theoph
Page 5 and 6: One compartment model (oral adminis
Page 7 and 8: One compartment model (oral adminis
Page 9 and 10: Viral load decrease during anti-HIV
Page 11 and 12: Viral load decrease during anti-HIV
Page 13 and 14: Evolution of the weight of 560 cows
Page 15 and 16: Le modèle mixte On aimerait constr
Page 17 and 18: Les modèles à effets mixtes Smina
Page 19 and 20: The (nonlinear) mixed effects model
Page 27 and 28: Objectifs Estimation Estimer l’en
Page 29 and 30: Objectifs Estimation Estimer l’en
Page 31 and 32: Les modèles mixtes en plein boom .
Page 33 and 34: Le groupe de travail MONOLIX Groupe
Page 35 and 36: Le groupe de travail MONOLIX Groupe
Page 37 and 38: Le groupe de travail MONOLIX des th
Page 39 and 40: Le groupe de travail MONOLIX des th
Page 41 and 42: Les méthodes d’estimation exista
Page 43 and 44: Some existing methods 1. Methods ba
Page 49 and 50: The incomplete data model The compl
Page 51 and 52: The incomplete data model The compl
Page 53 and 54: The EM algorithm (Expectation-Maxim
Page 55: The EM algorithm (Expectation-Maxim
Page 59 and 60: Convergence of EM Dempster et al. (
Page 61 and 62: The SAEM algorithm (Stochastic Appr
Page 63 and 64: The SAEM algorithm (Stochastic Appr
Page 65 and 66: Convergence of SAEM Delyon, Laviell
Page 71 and 72: A Simulated Annealing version of SA
Page 73 and 74: A Simulated Annealing version of SA
Page 75 and 76: Coupling SAEM and MCMC (Kuhn and La
Page 77 and 78: Coupling SAEM and MCMC (Kuhn and La
Page 79 and 80: Convergence of the algorithm (Kuhn
Page 81 and 82: Estimation of the Fisher Informatio
Page 83 and 84: Estimation of the Fisher Informatio
Page 85 and 86: Estimation of the likelihood Import
Page 87 and 88: Estimation of the likelihood Import
Page 89 and 90: Le logiciel MONOLIX Sminaire ”Sta
Page 91 and 92: Le logiciel MONOLIX A pharmacokinet
Page 93 and 94: Modèle défini par un système d
Page 95 and 96: Modèle mixte avec équation diffé
Page 97 and 98: Schéma numérique Utilisation d’
Page 99 and 100: Schéma numérique Utilisation d’
Page 101 and 102: Utilisation d’un modèle approch
Page 103 and 104: Quelques propriétés des algorithm
Page 105 and 106: Un schéma de linéarisation locale
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Model defined by ordinary different
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Exemple sur des données simulées
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Modèle défini par un système d
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Algorithme de Hastings-Metropolis A
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Application aux données de theophy
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Application aux données de theophy
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Conclusion Sminaire ”Statistique
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Conclusion Les modèles (non linéa
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Conclusion Les modèles (non linéa
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Les modÃ¨les non linÃ©aires Ã effets mixtes - Isped

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