This work proposes a sequential procedure to select the best model among several nested non-linear models and to estimate efficiently the parameters of the chosen model. At the first step of this procedure, a generalized DKL-optimum design is computed that is optimal for the goals of model selection and parameter estimation. Subsequently, at each step, an adaptive generalized DKL-optimum design is computed from the data accrued and the tests previously performed. The proposed sequential scheme selects the best non-linear model with probability converging to one; moreover it allows efficient estimates of parameters, since the adaptive sequential DKL-optimum designs converge to the D-optimum design for the "true" model.
|Titolo:||Model selection and parameter estimation in non-linear nested models : a sequential generalized DKL-optimum design|
TOMMASI, CHIARA (Ultimo)
|Parole Chiave:||argmin processes; convexity; D-optimality; DKL-optimality; KL-optimality;lLog-likelihood ratio test; semi-continuity; sequential design of experiments; stochastic convergence|
|Settore Scientifico Disciplinare:||Settore SECS-S/01 - Statistica|
|Data di pubblicazione:||2014|
|Digital Object Identifier (DOI):||10.5705/ss.2012.258|
|Appare nelle tipologie:||01 - Articolo su periodico|