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[The regression model and the nonlinear autoregressive processes].

J R León1

  • 1Universidad Central de Venezuela, Escuela de Matemática, Apartado Postal 47197, Los Chaguaramos, Caracas 1041-A, Venezuela. jleon@euler.ciens.ucv.ve

Acta Cientifica Venezolana
|March 27, 2001
PubMed
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This review covers regression models with fixed and random effects, detailing parametric and nonparametric estimation methods. It explores deducing nonlinear autoregressive models from regression under specific mixing conditions.

Area of Science:

  • Statistics
  • Econometrics

Context:

  • Regression analysis is fundamental in statistical modeling.
  • Fixed and random effects models are widely used for analyzing grouped data.

Purpose:

  • To review various regression models, including those with fixed and random effects.
  • To discuss parametric and nonparametric estimation techniques for these models.
  • To explore the connection between regression and nonlinear autoregressive models.

Summary:

  • The article examines regression models incorporating fixed and random effects.
  • It details standard parametric and nonparametric estimation approaches.
  • It demonstrates how nonlinear autoregressive models can be derived from regression models, provided certain mixing conditions are met.

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Impact:

  • Provides a comprehensive overview of regression model estimation techniques.
  • Offers insights into the relationship between different statistical modeling approaches.
  • Facilitates a deeper understanding of nonlinear time series analysis.