论文检索

时间序列中的冲击诊断AI 解读

Efficient means of modeling aberrant behavior in times series are developed. Our methods are based on state-space forms and allow test statistics for various interventions to be computed from a single run of the Kalman filter smoother. The approach encompasses existing detection methodologies. Departures commonly observed in practice, such as outlying values, level shifts, and switches, are readily dealt with. New diagnostic statistics are proposed. Implications for…

时间序列模型的模拟平滑器AI 解读

Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding the degeneracies inherent in state samplers. The technique is important in Gibbs sampling with non-Gaussian time series models, and for performing Bayesian analysis of Gaussian…

时间序列模型的模拟平滑器AI 解读

Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding the degeneracies inherent in state samplers. The technique is important in Gibbs sampling with non-Gaussian time series models, and for performing Bayesian analysis of Gaussian…

摘要暂缺

弥散卡尔曼滤波AI 解读

The Kalman recursion for state space models is extended to allow for likelihood evaluation and minimum mean square estimation given states with an arbitrarily large covariance matrix. The extension is computationally minor. Application is made to likelihood evaluation, state estimation, prediction and smoothing.

状态空间模型中的平滑与插值AI 解读

A new result dealing with smoothing and interpolation in the state-space model is developed and explored. The result simplifies the derivation of existing smoothing algorithms and provides alternate forms that have analytic and practical computing advantages. The connection to signal extraction and interpolation is explored, and diffuse specifications are considered.

时间序列模型的交叉验证滤波器AI 解读

A filter is presented which computes cross-validation errors and associated statistics for an arbitrary state space model. The procedure is more efficient than an existing approach. Diffuse initial conditions are easily handled using a minor extension. The relationship to the fixed interval smoothing algorithm is investigated.

状态空间模型中平滑估计的协方差AI 解读

Ansley & Kohn (1982) presented a novel proof of the fixed interval smoothing algorithm for state space models. In this note the ideas of their proof are used to provide a new formula for the covariance between smoothed estimates at any two points in time. This equation is substantially simpler than existing special cases.

摘要暂缺

状态空间模型中平滑估计的协方差AI 解读

Ansley & Kohn (1982) presented a novel proof of the fixed interval smoothing algorithm for state space models. In this note the ideas of their proof are used to provide a new formula for the covariance between smoothed estimates at any two points in time. This equation is substantially simpler than existing special cases.

时间序列模型的交叉验证滤波器AI 解读

A filter is presented which computes cross-validation errors and associated statistics for an arbitrary state space model. The procedure is more efficient than an existing approach. Diffuse initial conditions are easily handled using a minor extension. The relationship to the fixed interval smoothing algorithm is investigated.

状态空间模型的似然AI 解读

This paper derives an expression for the likelihood for a state space model. The expression can be evaluated with the Kalman filter initialized at a starting state estimate of zero and associated estimation error covariance matrix of zero. Adjustment for initial conditions can be made after filtering. Accordingly, initial conditions can be modelled without filtering implications. In particular initial conditions can be modelled as ‘diffuse’. The connection between the…

The efficient handling of provisional economic time series data is considered. Procedures are justified in terms of a testable hypothesis regarding the nature of data revisions. The hypothesis leads to explicit and practical variance expressions for measurement errors. Testing and estimation issues are dealt with. An efficient dual filter is developed for recursive signal estimation. Techniques are applied to the Canadian index of production data.

摘要暂缺

随机配对的检验AI 解读

The analysis of contingency tables in which both margins have the same polychotomy is considered. These tables arise when members from categories form pairs. Pairs cannot be between members of the same category. Appropriate models for pairing are introduced. A maximum likelihood algorithm is developed and asymptotic covariance formulas are derived. An empirical example illustrates the procedures.

摘要暂缺

摘要暂缺

自回归模型的递归拟合AI 解读

An algorithm for the recursive fitting of autoregressions of successively higher order is refined and extended. The quantities ocourring in the iterations of the algorithm are characterized as parameters of a moving average type representation which has a characterization precisely converse to that of the ordinary moving average representation usually associated with a stationary time series. An empirical example illustrates the use of the algorithm.