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http://dx.doi.org/10.5351/KJAS.2015.28.2.159

Variational Mode Decomposition with Missing Data  

Choi, Guebin (Department of Statistics, Seoul National University)
Oh, Hee-Seok (Department of Statistics, Seoul National University)
Lee, Youngjo (Department of Statistics, Seoul National University)
Kim, Donghoh (Department of Applied Mathematics, Sejong University)
Yu, Kyungsang (Department of Clinical Pharmacology and Therapeutics, Seoul National University)
Publication Information
The Korean Journal of Applied Statistics / v.28, no.2, 2015 , pp. 159-174 More about this Journal
Abstract
Dragomiretskiy and Zosso (2014) developed a new decomposition method, termed variational mode decomposition (VMD), which is efficient for handling the tone detection and separation of signals. However, VMD may be inefficient in the presence of missing data since it is based on a fast Fourier transform (FFT) algorithm. To overcome this problem, we propose a new approach based on a novel combination of VMD and hierarchical (or h)-likelihood method. The h-likelihood provides an effective imputation methodology for missing data when VMD decomposes the signal into several meaningful modes. A simulation study and real data analysis demonstrates that the proposed method can produce substantially effective results.
Keywords
Empirical mode decomposition; FFT; h-likelihood; missing data; variational mode decomposition;
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