Advances and Applications in Statistics
Volume 39, Issue 2, Pages 109 - 123
(April 2014)
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ESTIMATION OF MISSING VALUES IN LINEAR MODELS
Yorghos Tripodis and Nagaraj K. Neerchal
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Abstract: Missing observations are a common feature of many time series. We show that the pattern of the missing values is important for selecting an estimation method that results in small variance. If missing values are in consecutive time points, then using the ‘true’ data generating process produces estimates with much lower MSE. On the other hand, the performance of a ‘wrong’ model, such as the cubic spline, is acceptable in the case of random non-consecutive missing values. The differences in the performance of the cubic spline are validated through simulations for the case where the ‘true’ model is an AR(1). |
Keywords and phrases: missing values, cubic spline, interpolation, Kalman filter. |
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