Missing data in time series: A note on the equivalence of the dummy variable and the skipping approaches

被引：2

作者：

Proietti, Tommaso ^{[1
]}

机构：

[1] Dipartimento SEF & MEQ, I-00133 Rome, Italy

来源：

STATISTICS & PROBABILITY LETTERS | 2008年 / 78卷 / 03期

关键词：

Kalman filter; smoothing; influence; cross-validation;

D O I：

10.1016/j.spl.2007.05.031

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This note shows the equivalence of the dummy variable approach and the skipping approach for the treatment of missing observations in state space models. The equivalence holds when the coefficient of the dummy variable is considered as a diffuse rather than a fixed effect. The equivalence concerns both likelihood inference and smoothed inferences. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：257 / 264

页数：8

共 13 条

[1]

BELL WR, 1989, J R STAT SOC B, V51, P408

[2]

BRUCE AG, 1989, J ROY STAT SOC B MET, V51, P363

[3] Diagnosing shocks in time series [J].