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摘要暂缺

季节性移动平均模型的快速滤波AI 解读

Pearlman (1980) gives a fast filtering algorithm for an ARMA, i.e. autoregressive-moving average, model. When the algorithm is applied to a seasonal moving average model significant computational savings can be obtained by taking advantage of the structural zeros noted by Kohn & Ansley (1984) and Melard (1984). In this paper we identify a second set of structural zeros which leads to further significant computational savings. Our results can be applied to produce a fast…

参数经估计的状态空间模型预测均方误差AI 解读

We obtain a conditional prediction mean squared error for a state space model with estimated parameters. An important application of our results is the derivation of conditional forecast and interpolation mean squared errors for autoregressive-moving average models with estimated parameters. We also obtain the conditional mean squared error for filtered and smoothed estimates of the state vector.

季节性移动平均模型的快速滤波AI 解读

Pearlman (1980) gives a fast filtering algorithm for an ARMA, i.e. autoregressive-moving average, model. When the algorithm is applied to a seasonal moving average model significant computational savings can be obtained by taking advantage of the structural zeros noted by Kohn & Ansley (1984) and Melard (1984). In this paper we identify a second set of structural zeros which leads to further significant computational savings. Our results can be applied to produce a fast…

初始条件未完全指定的状态空间模型：估计、滤波与平滑AI 解读

The likelihood is defined for a state space model with incompletely specified initial conditions by transforming the data to eliminate the dependence on the unspecified conditions. This approach is extended to obtain estimates of the state vectors and predictors and interpolators for missing observations. It is then shown that this method is equivalent to placing a diffuse prior distribution on the unspecified part of the initial state vector, and modified versions of the…

时间序列回归模型的高效估计与预测AI 解读

For a time series regression model with correlated residuals we show how to compute efficiently the likelihood, predict future values and interpolate missing values. As an illustration we consider a regression model with autoregressive moving average residuals.

移动平均过程新息表示的收敛速度AI 解读

A moving average process of order q can be written as a linear combination of the (q+1) most recent innovations. We show that if the moving average polynomial has roots on the unit circle, then the coefficients of the innovations tend to the true moving average coefficients, the difference between the two being O (1/t), where t is the number of observations we consider. The results are extended to mixed autoregressive moving average models and multivariate models.

时间序列回归模型中的高效估计与预测AI 解读

For a time series regression model with correlated residuals we show how to compute efficiently the likelihood, predict future values and interpolate missing values. As an illustration we consider a regression model with autoregressive moving average residuals.

移动平均过程创新表示的收敛速度AI 解读

SUMMARY A moving average process of order q can be written as a linear combination of the (q+ 1) most recent innovations. We show that if the moving average polynomial has roots on the unit circle, then the coefficients of the innovations tend to the true moving average coefficients, the difference between the two being O(l/t), where t is the number of observations we consider. The results are extended to mixed autoregressive moving average models and multivariate models.

季节移动平均模型的卡尔曼滤波注记AI 解读

We show that if we apply the Kalman filter to a pure seasonal moving average model with no missing observations, then, by using a result of Ansley (1979), we can obtain significant computational savings.

季节移动平均模型的卡尔曼滤波札记AI 解读

We show that if we apply the Kalman filter to a pure seasonal moving average model with no missing observations, then, by using a result of Ansley (1979), we can obtain significant computational savings.

部分数据缺失或聚合时状态空间模型的固定区间估计AI 解读

Ansley & Kohn's (1982) geomertical derivation of the Kalman filter fixed-interval smoothing algorthm is extended to the case where some of the observations are missing or aggregated and the state covariance matrix may be singular. Estimates of missing observations are also obtained. The theory is then applied to the interpolation of missing data in mulativariate autoregressive-moving average modles.

部分数据缺失或聚合时状态空间模型的固定区间估计AI 解读

Ansley & Kohn's (1982) geomertical derivation of the Kalman filter fixed-interval smoothing algorthm is extended to the case where some of the observations are missing or aggregated and the state covariance matrix may be singular. Estimates of missing observations are also obtained. The theory is then applied to the interpolation of missing data in mulativariate autoregressive-moving average modles.

固定区间平滑算法的几何推导AI 解读

The Kalman filter fixed-interval smoothing algorithm is derived from geometrical considerations. This approach is considerably simpler than standard methods.

摘要暂缺

固定区间平滑算法的几何推导AI 解读

The Kalman filter fixed-interval smoothing algorithm is derived from geometrical considerations. This approach is considerably simpler than standard methods.