Dynamic Penalized Splines for Streaming Data
Copyright policy )Dynamic Penalized Splines for Streaming Data
Summary: We propose a dynamic version of the penalized spline regression designed for streaming data that allows for the insertion of new knots dynamically based on sequential updates of the summary statistics. A new theory using direct functional methods rather than the traditional matrix analysis is developed to attain the optimal convergence rate in the \(L^2\) sense for the dynamic estimation (also applicable for standard penalized splines) under weaker conditions than those in existing works on standard penalized splines.
convergence rate, nonparametric regression, Asymptotic properties of nonparametric inference, Nonparametric regression and quantile regression, streaming data, Numerical computation using splines
convergence rate, nonparametric regression, Asymptotic properties of nonparametric inference, Nonparametric regression and quantile regression, streaming data, Numerical computation using splines
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