Robinson, P.M. and Dunsmuir, W. (1981) Estimation of Time Series Models in the Presence of Missing Data. Journal of the American Statistical Association, 76, 560-568.
https://doi.org/10.1080/01621459.1981.10477687

has been cited by the following article:

• TITLE: Comparison of Methods of Estimating Missing Values in Time Series

  AUTHORS: I. S. Iwueze, E. C. Nwogu, V. U. Nlebedim, U. I. Nwosu, U. E. Chinyem

  KEYWORDS: Missing Values, Buys-Ballot Table, Row and Column Averages, Row and Column Variances, Trend Parameters and Seasonal Indices

  JOURNAL NAME: Open Journal of Statistics, Vol.8 No.2, April 30, 2018

  ABSTRACT: This paper proposes new methods of estimating missing values in time series data while comparing them with existing methods. The new methods are based on the row, column and overall averages of time series data arranged in a Buys-Ballot table with m rows and s columns. The methods assume that 1) only one value is missing at a time, 2) the trending curve may be linear, quadratic or exponential and 3) the decomposition method is either Additive or Multiplicative. The performances of the methods are assessed by comparing accuracy measures (MAE, MAPE and RMSE) computed from the deviations of estimates of the missing values from the actual values used in simulation. Results show that, under the stated assumptions, estimates from the new method based on full decomposition of a series is the best (in terms of the accuracy measures) when compared with other two new and the existing methods.