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Modified LASSO estimators for time series regression models with dependent disturbances

机译:改进的套索估算时间阶段序列回归模型具有依赖干扰

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摘要

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.
机译:本文将改进的最小绝对收缩和选择操作员(套索)应用于回归模型,尤其是长记忆扰动。假设回归矩阵中的不同列的规范可以具有不同的观察长度n,我们引入了修改的套索估计器,其中调谐参数λ不是标量而不是向量。当参数的维度固定时,我们在某些规则性条件下导出了改进的套索估计的渐近分布。当参数的尺寸相对于N增加时,在某些规则性条件下显示正确选择惩罚参数的概率的一致性。检查了一些仿真研究。

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