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Numrering	Referens	Omslagsbild	Hitta
1.	Abdalmoaty, Mohamed, 1986-, et al. (författare) Identification of Stochastic Nonlinear Models Using Optimal Estimating Functions 2020 Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 119 Tidskriftsartikel (refereegranskat)abstract The first part of the paper examines the asymptotic properties of linear prediction error method estimators, which were recently suggested for the identification of nonlinear stochastic dynamical models. It is shown that their accuracy depends not only on the shape of the unknown distribution of the data, but also on how the model is parameterized. Therefore, it is not obvious in general which linear prediction error method should be preferred. In the second part, the estimating functions approach is introduced and used to construct estimators that are asymptotically optimal with respect to a specific class of estimators. These estimators rely on a partial probabilistic parametric models, and therefore neither require the computations of the likelihood function nor any marginalization integrals. The convergence and consistency of the proposed estimators are established under standard regularity and identifiability assumptions akin to those of prediction error methods. The paper is concluded by several numerical simulation examples.

Abdalmoaty, Mohamed, 1986-, et al. (författare)
Identification of Stochastic Nonlinear Models Using Optimal Estimating Functions
2020
Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 119
Tidskriftsartikel (refereegranskat)abstract
- The first part of the paper examines the asymptotic properties of linear prediction error method estimators, which were recently suggested for the identification of nonlinear stochastic dynamical models. It is shown that their accuracy depends not only on the shape of the unknown distribution of the data, but also on how the model is parameterized. Therefore, it is not obvious in general which linear prediction error method should be preferred. In the second part, the estimating functions approach is introduced and used to construct estimators that are asymptotically optimal with respect to a specific class of estimators. These estimators rely on a partial probabilistic parametric models, and therefore neither require the computations of the likelihood function nor any marginalization integrals. The convergence and consistency of the proposed estimators are established under standard regularity and identifiability assumptions akin to those of prediction error methods. The paper is concluded by several numerical simulation examples.

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