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Les conditions d’identifiabilité Modèle non linéaire Nouvel estimateur de F (x, y) Modèle de régression single-indexHypothèses de convergence de ˆfComparaison avec les méthodes existantesConfig 1 Config 2 Config 3ε ∼ N (0, 2) ε ∼ N (0, 1) ε ∼ N (0, 1/16)X ∼ U[−2; 2] ⊗ U[−2; 2] X ∼ U[0; 1] ⊗ U[0; 1] X ∼ B(0.6) ⊗ U[−1; 1]f (θ ′ x; θ) = 1/2(θ ′ x) 2 + 1 f (θ ′ x; θ) = 2e(0.5θ′ x)θ ′ xf (θ ′ x; θ) = 1 + 0.1(θ ′ x) 2−0.2(θ ′ x − 1)θ 0 = (1, 1) ′ θ 0 = (1, 2) ′ θ 0 = (1, 2) ′C ∼ U[0, λ 1 ] C ∼ E(λ 2 ) C ∼ E(λ 3 )
Les conditions d’identifiabilité Modèle non linéaire Nouvel estimateur de F (x, y) Modèle de régression single-indexHypothèses de convergence de ˆfConfig 1 n = 50 n = 100λ 1 = 1.17 ˆθ AD 3.3285 × 10 −2 1.8236 × 10 −2ˆθ MC 3.8088 × 10 −5 2.9482 × 10 −5λ 1 = 0.1 ˆθAD 7.4870 × 10 −2 5.0438 × 10 −2ˆθ MC 1.3010 × 10 −4 3.7669 × 10 −5Config 2 n = 50 n = 100λ 2 = 0.1 ˆθ AD 3.3522 × 10 −1 2.8713 × 10 −1ˆθ MC 7.8301 × 10 −3 7.7180 × 10 −3λ 2 = 0.05 ˆθAD 1.5553 1.5223ˆθ MC 1.5100 × 10 −2 1.2013 × 10 −2Config 3 n = 50 n = 100λ 3 = 4 ˆθ AD > 10 > 10ˆθ MC 3.3124 × 10 −4 2.8984 × 10 −4λ 3 = 2 ˆθ AD > 10 > 10ˆθ MC 1.1431 × 10 −2 2.4111 × 10 −4
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ProblÃ¨mes de rÃ©duction de dimension pour la rÃ©gression en ... - Isped

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