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Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider bootstrap methods for computing standard errors and con dence intervals that take model selecti...
Abstract: This work propose a sequential procedure which is useful to select the best model among several nested non-linear models and to estimate efficiently the parameters of the chosen model. At ea...
Abstract: We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on...
Abstract: The paper considers a linear regression model with multiple change-points occurring at unknown times. The LASSO technique is very interesting since it allows the parametric estimation, inclu...

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