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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Nonlinear autoregressive sieve bootstrap based on extreme learning machines.

Michele La Rocca1, Cira Perna1

  • 1Department of Economics and Statistics, University of Salerno, via Giovanni Paolo II, Fisciano 84084, Italy.

Mathematical Biosciences and Engineering : MBE
|November 17, 2019
PubMed
Summary

This study introduces a novel sieve bootstrap method using Extreme Learning Machines for nonlinear time series analysis. This approach offers computational efficiency and accurate results comparable to existing methods.

Keywords:
Monte Carloextreme learning machinesneural networksnonlinear time seriessieve bootstrap

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Area of Science:

  • Statistics
  • Machine Learning
  • Time Series Analysis

Background:

  • Nonlinear time series analysis presents challenges for traditional statistical methods.
  • Existing bootstrap methods can be computationally intensive.

Purpose of the Study:

  • To propose and discuss a sieve bootstrap scheme utilizing Extreme Learning Machines (ELMs).
  • To evaluate the performance and computational efficiency of the proposed ELM-based sieve bootstrap.

Main Methods:

  • The study employs a fully nonparametric sieve bootstrap scheme.
  • Extreme Learning Machines are integrated into the resampling process.
  • Monte Carlo simulations are used for performance evaluation and comparison.

Main Results:

  • The ELM-based sieve bootstrap demonstrates computational efficiency, reducing burden compared to NN-Sieve bootstrap.
  • Performance is comparable to NN-Sieve bootstrap and computing time is similar to ARSieve bootstrap.
  • Bootstrap variance estimators show consistency, accuracy, and low bias for various statistics.

Conclusions:

  • The proposed ELM-based sieve bootstrap is an effective and computationally efficient method for nonlinear time series.
  • It offers a viable alternative to existing bootstrap procedures, providing reliable estimation for diverse statistical measures.