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

Title & Authors
The Comparison of Imputation Methods in Time Series Data with Missing Values
Lee, Sung-Duck; Choi, Jae-Hyuk; Kim, Duck-Ki;

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 $\small{{\sim}}$ 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;
Language
Korean
Cited by
1.
공간시계열모형의 결측치 추정방법 비교,이성덕;김덕기;

Communications for Statistical Applications and Methods, 2010. vol.17. 2, pp.263-273
References
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

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

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