Modeling and Smoothing Unequally Spaced Sequence Data

被引：0
|
作者：
Piet de Jong
Sonia Mazzi
机构：
[1] University of British Columbia,Faculty of Commerce
[2] University of York,Department of Mathematics
来源：
Statistical Inference for Stochastic Processes | 2001年 / 4卷 / 1期
关键词：
Kalman filter; Splines; Smoothing; State space model;
D O I：
10.1023/A:1017510420686
中图分类号：
学科分类号：
摘要：
The application of the continuous state space model to unequally spaced sequence data is discussed and illustrated. The continuous model implies a discrete model for the observed data. Practical expressions for relevant discrete model quantities are given. These quantities are required for the digital processing of the data and in particular for the application of the Kalman and smoothing filter and related calculations. Applications illustrate the procedures.
引用
收藏
页码：53 / 71
页数：18
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