Browse > Article
http://dx.doi.org/10.5351/CKSS.2009.16.4.723

The Comparison of Imputation Methods in Time Series Data with Missing Values  

Lee, Sung-Duck (Department of Information and Statistics, Chungbuk National University)
Choi, Jae-Hyuk (Department of Statistics, Sung Kyun Kwan University)
Kim, Duck-Ki (Department of Information and Statistics, Chungbuk National University)
Publication Information
Communications for Statistical Applications and Methods / v.16, no.4, 2009 , pp. 723-730 More about this Journal
Abstract
Missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random variables and predicted by the expectation of the unknown values given the data. The purpose of this study is to impute missing values which are regarded as the maximum likelihood estimator and random variable in incomplete data and to compare with two methods using ARMA model. For illustration, the Mumps data reported from the national capital region monthly over the years 2001 ${\sim}$ 2006 are used, and results from two methods are compared with using SSF(Sum of square for forecasting error).
Keywords
Maximum likelihood estimation; random variables; ARMA model; Mumps data; sum of square for forecasting error;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bayarri, M. J., DeGroot, M. H. and Kadane, J. B. (1986). What is the Likelihood Function? In: Statistical Decision Theory and Related Topics IV, Volume 1., (S. S. Gupta and J. O. Berger eds), New York: Springer-Verlag
2 Box, G. E. P. and G. C. Tiao (1973). Bayesian Inference in Statistical Analysis, Reading, M. A, Addison-Wesley
3 Brubacher, S. R. and Wilson, T. (1976). Interpolating time series with application to the estimation of holiday effects on electricity demand, Applied statistics, 25, 107-116   DOI   ScienceOn
4 Dunsmuir, W. and Robinson, P. M. (1981). Estimation of time series models in the presence of missing data, Journal of the American Statistical Association, 76, 560-68   DOI   ScienceOn
5 Pena, D. and Tiao, G. C. (1991) A note on likelihood estimation of missing values in time series, The American Statistician, 45, 212-213   DOI   ScienceOn