title: Empirical mode decomposition with missing values.
abstract: This paper considers an improvement of empirical mode decomposition (EMD) in the presence of missing data. EMD has been widely used to decompose nonlinear and nonstationary signals into some components according to intrinsic frequency called intrinsic mode functions. However, the conventional EMD may not be efficient when missing values are present. This paper proposes a modified EMD procedure based on a novel combination of empirical mode decomposition and self-consistency concept. The self-consistency provides an effective imputation method of missing data, and hence, the proposed EMD procedure produces stable decomposition results. Simulation studies and the image analysis demonstrate that the proposed method produces substantially effective results.
authors.affiliation: Department of Applied Mathematics, Sejong University, Seoul, 05006 Korea.
authors.forename: Donghoh
authors.initials: D
authors.lastname: Kim
authors.affiliation: Department of Statistics, Seoul National University, Seoul, 08826 Korea.
authors.forename: Hee-Seok
authors.initials: HS
authors.lastname: Oh
keywords: Empirical mode decomposition; Imputation; Missing; Multiscale method; Self-consistency