A new Tweedie kernel estimator for semicontinuous mixed densities on [0, ∞) is proposed, with derived asymptotic properties and a cross-validation method for selecting bandwidth and power parameter.
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Introduces D-GM estimator for simplex regression, derives its bias/variance/asymptotics/MISE, and finds via simulation that the local-linear competitor outperforms both D-GM and D-NW.
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Tweedie-based nonparametric estimation for semicontinuous mixed densities
A new Tweedie kernel estimator for semicontinuous mixed densities on [0, ∞) is proposed, with derived asymptotic properties and a cross-validation method for selecting bandwidth and power parameter.
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On the Dirichlet-kernel Gasser--M\"uller estimator and its competitors for fixed design regression on the simplex
Introduces D-GM estimator for simplex regression, derives its bias/variance/asymptotics/MISE, and finds via simulation that the local-linear competitor outperforms both D-GM and D-NW.