期刊名称：American Journal of Applied Mathematics and Statistics
印刷版ISSN：2328-7306
电子版ISSN：2328-7292
出版年度：2015
卷号：3
期号：5
页码：199-205
DOI：10.12691/ajams-3-5-4
出版社：Science and Education Publishing
摘要：In this study, estimates of missing values for bilinear time series models with normally distributed innovations were derived by minimizing the h-steps-ahead dispersion error. For comparison purposes, missing value estimates based on artificial neural network (ANN) and exponential smoothing (EXP) techniques were also obtained. Simulated data was used in the study. 100 samples of size 500 each were generated for different pure bilinear time series models using the R-statistical software. In each sample, artificial missing observations were created at data positions 48, 293 and 496 and estimated using these methods. The performance criteria used to ascertain the efficiency of these estimates were the mean absolute deviation (MAD) and mean squared error (MSE). The study found that optimal linear estimates were the most efficient estimates for estimating missing values. Further, the optimal linear estimates were equivalent to one step-ahead forecast of the missing value. The study recommends OLE estimates for estimating missing values for pure bilinear time series data with normally distributed innovations.
关键词：optimal linear interpolation; simulation; MAD; innovations; ANN; exponential smoothing
