State Space Models in R

被引：0

作者：

Petris, Giovanni ^{[1
]}

Petrone, Sonia ^{[2
]}

机构：

[1] Univ Arkansas, Dept Math Sci, Fayetteville, AR 72701 USA

[2] Univ Bocconi, Dept Decis Sci, Milan, Italy

来源：

JOURNAL OF STATISTICAL SOFTWARE | 2011年 / 41卷 / 04期

关键词：

Kalman filter; state space models; unobserved components; software tools; R; SIMULATION SMOOTHER; PACKAGE;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

We give an overview of some of the software tools available in R, either as built-in functions or contributed packages, for the analysis of state space models. Several illustrative examples are included, covering constant and time-varying models for both univariate and multivariate time series. Maximum likelihood and Bayesian methods to obtain parameter estimates are considered.

引用

页码：1 / 25

页数：25

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