D
Dmitri Kondrashov
Researcher at University of California, Los Angeles
Publications - 71
Citations - 4652
Dmitri Kondrashov is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Stochastic modelling & Singular spectrum analysis. The author has an hindex of 26, co-authored 67 publications receiving 4220 citations. Previous affiliations of Dmitri Kondrashov include University of California & California Institute of Technology.
Papers
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Journal ArticleDOI
Advanced spectral methods for climatic time series
Michael Ghil,M. R. Allen,Michael D. Dettinger,Kayo Ide,Dmitri Kondrashov,Michael E. Mann,Andrew W. Robertson,Andrew W. Robertson,A. Saunders,Yudong Tian,F. Varadi,Pascal Yiou +11 more
TL;DR: The connections between time series analysis and nonlinear dynamics, discuss signal-to-noise enhancement, and present some of the novel methods for spectral analysis are described.
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Spatio-temporal filling of missing points in geophysical data sets
TL;DR: The algorithm is demonstrated on synthetic examples, as well as on data sets from oceanography, hydrology, atmospheric sciences, and space physics: global sea-surface temperature, flood-water records of the Nile River, the Southern Oscillation Index (SOI), and satellite observations of relativistic electrons.
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Extreme events: dynamics, statistics and prediction
Michael Ghil,Michael Ghil,Pascal Yiou,Stephane Hallegatte,Bruce D. Malamud,Philippe Naveau,A. Soloviev,Petra Friederichs,Vladimir Keilis-Borok,Dmitri Kondrashov,Vladimir Kossobokov,Olivier Mestre,C. Nicolis,Henning W. Rust,Peter Shebalin,Mathieu Vrac,Annette Witt,Annette Witt,Ilya Zaliapin +18 more
TL;DR: Two important results refer to the complementarity of spectral analysis of a time series in terms of the continuous and the discrete part of its power spectrum and the need for coupled modeling of natural and socio-economic systems.
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Multilevel Regression Modeling of Nonlinear Processes: Derivation and Applications to Climatic Variability
TL;DR: In this article, a multilevel generalization of the classic regression proce... is proposed, where the dependence on the regression parameters is linear in both MPR and MLR, and the basic concepts are illustrated using the Lorenz convection model, the classical double well problem, and a three-well problem.
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Data-driven non-Markovian closure models
TL;DR: In this paper, a multilayer stochastic model (MSM) is introduced to obtain stable and efficient data-driven closure models by using a multivariate time series of partial observations from a large-dimensional system; and comparing these closure models with the optimal closures predicted by the Mori-Zwanzig formalism of statistical physics.