论文检索

状态空间模型中的趋势估计：渐近均方误差与收敛速度AI 解读

The focus of this paper is on trend estimation for a general state-space model Yt=μt+ɛt, where the dth difference of the trend {μt} is assumed to be i.i.d., and the error sequence {ɛt} is assumed to be a mean zero stationary process. A fairly precise asymptotic expression of the mean square error is derived for the estimator obtained by penalizing the dth order differences. Optimal rate of convergence is obtained, and it is shown to be “asymptotically equivalent” to a…

多元时间序列的判别与聚类AI 解读

Minimum discrimination information provides a useful generalization of likelihood methodology for classification and clustering of multivariate time series. Discrimination between different classes of multivariate time series that can be characterized by differing covariance or spectral structures is of importance in applications occurring in the analysis of geophysical and medical time series data. For discrimination between such multivariate series, Kullback-Leibler…

利用动态回归模型进行预测AI 解读

A Primer on ARIMA Models. A Primer on Regression Models. Rational Distributed Lag Models. Building Dynamic Regression Models: Model Identification. Building Dynamic Regression Models: Model Checking, Reformulation, and Evaluation. Intervention Analysis. Intervention and Outlier Detection and Treatment. Estimation and Forecasting. Dynamic Regression Models in a Vector ARMA Framework. Appendices. References. Index.

暂定死亡率序列的调整：含结构化测量误差的动态线性模型AI 解读

The authors "consider the problem of adjusting provisional time series using a bivariate structural model with correlated measurement errors. Maximum likelihood estimators and a minimum mean squared error adjustment procedure are derived for a provisional and final series containing common trend and seasonal components. The model also includes measurement errors common to both series and errors that are specific to the provisional series. [The authors] illustrate the…

摘要暂缺

摘要暂缺

平稳时间序列的应用回归与方差分析AI 解读

In the area of applied time series analysis a commonly occurring problem involves the detection and estimation of signals (regression functions) imbedded within a collection of independent identically distributed noise processes. A general linear model which represents each observed time series as the sum of a wide sense stationary error process and a vector of regression functions operated on by a matrix of time invariant observables includes as special cases many signal…