Nonparametric estimation in models generated by SDEs with symmetric α-stable processes: a comparative study
Анотація
This work develops and compares several nonparametric estimators for the drift function in stochastic differential equations driven by symmetric α-stable processes. Kernel, Lp, and spline-based estimators are analyzed alongside Chebyshev best-fit methods, emphasizing smoothing, regularization, and comparative accuracy. The results show that Chebyshev-based fitting achieves improved stability and accuracy compared to spline and kernel estimators. A dedicated software is implemented to illustrate the performance of the proposed estimators across representative examples.
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