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自回归移动平均模型阶数估计札记AI 解读

A method for the estimation of parameters and model orders in an autoregressive-moving average system was introduced by Hannan & Rissanen (1982). That method may overestimate the model orders. Here an information theoretic order estimation procedure is introduced and shown to be consistent. Simulations show that the method performs well for moderate sample sizes.

线性系统的递归估计AI 解读

The estimation of parameters for a scalar linear system with rational transfer function is considered, the estimates being updated each time a new data point comes to hand. Two well-known algorithms are compared by simulation with a method related to the three-stage algorithm of Hannan & Rissanen (1982); a computation scheme using Givens transformations is proposed which makes the three-stage recursive algorithm economical. A central limit theorem is proved for the…

线性系统的递归估计AI 解读

The estimation of parameters for a scalar linear system with rational transfer function is considered, the estimates being updated each time a new data point comes to hand. Two well-known algorithms are compared by simulation with a method related to the three-stage algorithm of Hannan & Rissanen (1982); a computation scheme using Givens transformations is proposed which makes the three-stage recursive algorithm economical. A central limit theorem is proved for the…

自回归移动平均模型的一种估计方法AI 解读

The problem considered is that of estimating an autoregressive-moving average system, including estimating the degrees of the autoregressive and moving average lag operators. The basic method is that introduced by Hannan & Rissanen (1982). However, that method may sometimes overestimate the degrees and modifications are here introduced to correct this. The problem is itself due to the use of a long autoregression, of order clogT when T is large, in the first stage of the…

一种自回归移动平均模型估计方法AI 解读

The problem considered is that of estimating an autoregressive-moving average system, including estimating the degrees of the autoregressive and moving average lag operators. The basic method is that introduced by Hannan & Rissanen (1982). However, that method may sometimes overestimate the degrees and modifications are here introduced to correct this. The problem is itself due to the use of a long autoregression, of order clogT when T is large, in the first stage of the…