LEAST-SQUARES ESTIMATION OF MISSING VALUES IN TIME-SERIES

被引：19

作者：

BEVERIDGE, S ^{[1
]}

机构：

[1] UNIV ALBERTA,FAC BUSINESS,EDMONTON T6G 2R6,ALBERTA,CANADA

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 1992年 / 21卷 / 12期

关键词：

MISSING VALUES; LEAST SQUARES INTERPOLATION; TIME SERIES; ARIMA MODELS;

D O I：

10.1080/03610929208830990

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：

The minimum mean square error linear interpolator for missing values in time series is extended to handle any pattern of nonconsecutive observations. The paper then develops evidence with simple ARMA models that the usefulness of either the "nonparametric" or the parametric form of the least squares interpolator depends on the time series model, the arrangement of the missing data and the objective for completing the series.

引用

页码：3479 / 3496

页数：18

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