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August 6, 2026Stochastic Models

Asymptotic properties for the wavelet estimator in nonparametric regression models under infinite p -th moments

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Authors

CLChao LüHefei University of TechnologyXWXuejun WangAnhui University

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Implication

Demonstrates mean and strong consistency of wavelet estimators in nonparametric regression, suggesting improved methodologies.

Key Points

  • The aim is to study the wavelet estimator's properties in nonparametric regression with infinite p-th moments.
  • Analyzed wavelet estimator in a nonparametric regression model with independent random errors.
  • Investigated mean and strong consistency for 1<p<2 without requiring finite p-th moments.
  • Conducted numerical simulations to validate theoretical findings.
  • Established mean consistency and strong consistency of the wavelet estimator under the specified conditions.
  • Improved existing results in the literature regarding wavelet estimators.
  • Numerical simulations confirmed the theoretical results and their finite sample performance.

Cite This Study

Lü et al. (2026) studied this question.

synapsesocial.com/papers/6a743783764cddc9499d4f23https://doi.org/10.1080/15326349.2026.2706479
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