This work proposes a sequential procedure which is useful to select the best model among several nested non-linear models and to estimate eciently the parameters of the chosen model. At the rst step of this procedure, a general- ized DKL-optimum design is computed, which is optimal for the double goal of model selection and parameter estimation. Subsequently, at each following step, an adaptive generalized DKL-optimum design is computed on the base of the data accrued and tests previously performed. The proposed sequential scheme selects the best non-linear model with probability converging to one; moreover it estimates eciently its parameters, since the adaptive sequen- tial DKL-optimum designs converge to the D-optimum design for the \true" model.
Model selection and parameter estimation in non-linear nested models : a sequential generalized DKL-optimum design / C. May, C. Tommasi. - [s.l] : S.n., 2011.
Model selection and parameter estimation in non-linear nested models : a sequential generalized DKL-optimum design
C. Tommasi
2011
Abstract
This work proposes a sequential procedure which is useful to select the best model among several nested non-linear models and to estimate eciently the parameters of the chosen model. At the rst step of this procedure, a general- ized DKL-optimum design is computed, which is optimal for the double goal of model selection and parameter estimation. Subsequently, at each following step, an adaptive generalized DKL-optimum design is computed on the base of the data accrued and tests previously performed. The proposed sequential scheme selects the best non-linear model with probability converging to one; moreover it estimates eciently its parameters, since the adaptive sequen- tial DKL-optimum designs converge to the D-optimum design for the \true" model.Pubblicazioni consigliate
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