期刊
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
卷 55, 期 1, 页码 197-216出版社
KLUWER ACADEMIC PUBL
DOI: 10.1007/BF02530494
关键词
autoregressive process; innovation departure; linear interpolation; minimum mean absolute error; missing values
We propose a minimum mean absolute error linear interpolator (MMAELI), based on the L-1 approach. A linear functional of the observed time series due to non-normal innovations is derived. The solution equation for the coefficients of this linear functional is established in terms of the innovation series. It is found that information implied in the innovation series is useful for the interpolation of missing values. The MMAELIs of the AR(1) model with innovations following mixed normal and t distributions are studied in detail. The MMAELI also approximates the minimum mean squared error linear interpolator (MMSELI) well in mean squared error but outperforms the MMSELI in mean absolute error. An application to a real series is presented. Extensions to the general ARMA model and other time series models axe discussed.
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