237 citations · 464 across the 14 of their papers we have counts for
16 papers
Filling the gap in the IERS C01 polar motion series in 1858.9-1860.9
Zinovy Malkin, Nina Golyandina, Roman Olenev
The C01 Earth orientation parameters (EOP) series provided by the International Earth Rotation and Reference Systems Service (IERS) is the longest reliable record of the Earth's ro…
On automated identification in singular spectrum analysis for different types of objects
Nina Golyandina, Polina Zhornikova
Approaches to automated grouping in singular spectrum analysis are considered. A new method for the identification of periodic components is proposed. The possibilities of extensio…
Fast and stable modification of the Gauss-Newton method for low-rank signal estimation
Nikita Zvonarev, Nina Golyandina
The weighted nonlinear least-squares problem for low-rank signal estimation is considered. The problem of constructing a numerical solution that is stable and fast for long time se…
Low-rank signal subspace: parameterization, projection and signal estimation
Nikita Zvonarev, Nina Golyandina
The paper contains several theoretical results related to the weighted nonlinear least-squares problem for low-rank signal estimation, which can be considered as a Hankel structure…
Blind deconvolution of covariance matrix inverses for autoregressive processes
Nina Golyandina, Anatoly Zhigljavsky
Matrix can be blindly deconvoluted if there exist matrices and such that , where denotes the o…
Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing
Nina Golyandina
Singular spectrum analysis (SSA), starting from the second half of the XX century, has been a rapidly developing method of time series analysis. Since it can be called principal co…
Detection of signals by Monte Carlo singular spectrum analysis: Multiple testing
Nina Golyandina
Detection of a signal in a noisy time series using Monte Carlo singular spectrum analysis (MC-SSA) is studied from the statistical viewpoint. The MC-SSA test consists of simultaneo…
Image space projection for low-rank signal estimation: Modified Gauss-Newton method
N. Zvonarev, N. Golyandina
The paper is devoted to the solution of a weighted nonlinear least-squares problem for low-rank signal estimation, which is related to Hankel structured low-rank approximation prob…
Two-exponential models of gene expression patterns for noisy experimental data
Theodore Alexandrov, Nina Golyandina, David Holloway +2
Motivation: Spatial pattern formation of the primary anterior-posterior morphogenetic gradient of the transcription factor Bicoid (Bcd) has been studied experimentally and computat…
Semi-nonparametric singular spectrum analysis with projection
Nina Golyandina, Alex Shlemov
Singular spectrum analysis (SSA) is considered for decomposition of time series into identifiable components. The Basic SSA method is nonparametric and constructs an adaptive expan…
Iterative algorithms for weighted and unweighted finite-rank time-series approximations
Nikita Zvonarev, Nina Golyandina
The problem of time series approximation by series of finite rank is considered from the viewpoint of signal extraction. For signal estimation, a weighted least-squares method is a…
Shaped extensions of singular spectrum analysis
Alex Shlemov, Nina Golyandina
Extensions of singular spectrum analysis (SSA) for processing of non-rectangular images and time series with gaps are considered. A circular version is suggested, which allows appl…
Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package
Nina Golyandina, Anton Korobeynikov, Alex Shlemov +1
Implementation of multivariate and 2D extensions of Singular Spectrum Analysis (SSA) by means of the R-package Rssa is considered. The extensions include MSSA for simultaneous anal…
Variations of singular spectrum analysis for separability improvement: non-orthogonal decompositions of time series
Nina Golyandina, Alex Shlemov
Singular spectrum analysis (SSA) as a nonparametric tool for decomposition of an observed time series into sum of interpretable components such as trend, oscillations and noise is…
Basic Singular Spectrum Analysis and Forecasting with R
Nina Golyandina, Anton Korobeynikov
Singular Spectrum Analysis (SSA) as a tool for analysis and forecasting of time series is considered. The main features of the Rssa package, which implements the SSA algorithms and…
On the choice of parameters in Singular Spectrum Analysis and related subspace-based methods
Nina Golyandina
In the present paper we investigate methods related to both the Singular Spectrum Analysis (SSA) and subspace-based methods in signal processing. We describe common and specific fe…