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Robust regularized singular value decomposition with application to mortality data
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1 of the 32 references listed on this page is in the Retraction Watch database as retracted (export 2026-10-28): 10.1016/j.csda.2009.07.024 (notice 2010-10-01).
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Applied Functional Data Analysis: Methods and Case Studies
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Robust principal component analysis for functional data
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Some results on Tchebycheffian spline functions
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Robust forecasting of mortality and fertility rates: A functional data approach
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RETRACTED: The functional singular value decomposition for bivariate stochastic processes
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Forecasting functional time series
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Robust singular value decomposition analysis of microarray data
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Lower Rank Approximation of Matrices by Least Squares With Any Choice of Weights
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Least Median of Squares Regression
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Robust Singular Value Decompositions: A New Approach to Projection Pursuit
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Design of anSFunction for Robust Regression Using Iteratively Reweighted Least Squares
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EOF Calculations and Data Filling from Incomplete Oceanographic Datasets*
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Functional Data Analysis for Sparse Longitudinal Data
inferredcites →https://doi.org/10.1198/106186007x256080openalex-snapshot
Singular Value Decomposition and Its Visualization
inferredcites →https://doi.org/10.1198/jasa.2009.tm08024openalex-snapshot
The Analysis of Two-Way Functional Data Using Two-Way Regularized Singular Value Decompositions
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Nonparametric Regression and Generalized Linear Models
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Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals
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Detecting and handling outlying trajectories in irregularly sampled functional datasets
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Sparse logistic principal components analysis for binary data
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Robust functional principal components: A projection-pursuit approach
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Smoothed functional principal components analysis by choice of norm
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A SUPERVISED SINGULAR VALUE DECOMPOSITION FOR INDEPENDENT COMPONENT ANALYSIS OF fMRI
inferredcites →https://doi.org/10.4310/sii.2011.v4.n4.a10openalex-snapshot
A spatio-temporal solution for the EEG/MEG inverse problem using group penalization methods
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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