Autor(es)

Sousa, Alex Rodrigo dos S. ; Zevallos, Mauricio

• Tipo de documento: Artigo Científico
• Fonte: STATISTICS AND COMPUTING; v. 35, n. 4, p. 14-pg., 2025-04-27.

Resumo

This work proposes a Bayesian rule based on the mixture of a point mass function at zero and the logistic distribution to perform wavelet shrinkage in nonparametric regression models with stationary errors (with short or long-memory behavior). The proposal is assessed through Monte Carlo experiments and illustrated with real data. Simulation studies indicate that the precision of the estimates decreases as the amount of correlation increases. However, given a sample size and error correlated noise, the performance of the rule is almost the same while the signal-to-noise ratio decreases, compared to the performance of the rule under independent and identically distributed errors. Further, we find that the performance of the proposal is better than the standard soft thresholding rule with universal policy in most of the considered underlying functions, sample sizes and signal-to-noise ratios scenarios. (AU)

• Processo FAPESP: 23/02538-0 - Séries temporais, ondaletas, dados de alta dimensão e aplicações
• Beneficiário: Aluísio de Souza Pinheiro
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 23/01728-0 - Modelagem e previsão econométrica em modelos de alta dimensão
• Beneficiário: Pedro Luiz Valls Pereira
• Modalidade de apoio: Auxílio à Pesquisa - Temático