Modified LASSO estimators for time series regression models with dependent disturbances

研究成果: Article査読

1 被引用数 (Scopus)

抄録

This paper applies the modified least absolute shrinkage and selection operator (LASSO) to the regression model with dependent disturbances, especially, long-memory disturbances. Assuming the norm of different column in the regression matrix may have different order of observation length n, we introduce a modified LASSO estimator where the tuning parameter λ is not a scalar but vector. When the dimension of parameters is fixed, we derive the asymptotic distribution of the modified LASSO estimators under certain regularity condition. When the dimension of parameters increases with respect to n, the consistency on the probability of the correct selection of penalty parameters is shown under certain regularity conditions. Some simulation studies are examined.

本文言語English
ページ(範囲)845-869
ページ数25
ジャーナルStatistical Methods and Applications
29
4
DOI
出版ステータスPublished - 2020 12

ASJC Scopus subject areas

  • 統計学および確率
  • 統計学、確率および不確実性

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