Nonparametric adaptive estimation of linear functionals for low frequency observed LÃ©vy processes
For a LÃ©vy process X having finite variation on compact sets and finite first moments, Âµ( dx) = xv( dx) is a finite signed measure which completely describes the jump dynamics. We construct kernel estimators for linear functionals of Âµ and provide rates of convergence under regularity assumptions. Moreover, we consider adaptive estimation via model selection and propose a new strategy for the data driven choice of the smoothing parameter.
|Date of creation:||Feb 2012|
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