Results 11 to 20 of about 1,596,868 (270)
A robust sparse representation algorithm based on adaptive joint dictionary
CAAI Transactions on Intelligence Technology, 2023 Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face recognition. Aiming at the shortcomings of face feature dictionary not ‘clean’ and noise interference dictionary not ‘representative ...
Ying Tong +3 more
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Adaptive Robust Regression by Using a Nonlinear Regression Program
Journal of Statistical Software, 1999 Robust regression procedures have considerable attention in mathematical statistics literature. They, however, have not received nearly as much attention by practitioners performing data analysis.
Mortaza Jamshidian
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Robust reduced-rank regression [PDF]
Biometrika, 2017 SummaryIn high-dimensional multivariate regression problems, enforcing low rank in the coefficient matrix offers effective dimension reduction, which greatly facilitates parameter estimation and model interpretation. However, commonly used reduced-rank methods are sensitive to data corruption, as the low-rank dependence structure between response ...
She, Y., Chen, Kun
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IEEE Access, 2021 The statistically inspired modification of the partial least squares (SIMPLS) is the most commonly used algorithm to solve a partial least squares regression problem when the number of explanatory variables ( $p$ ) is larger than the sample size ( $n$ ).
Abdullah Mohammed Rashid +3 more
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Impact of MPC Embedded Performance Index on Control Quality
IEEE Access, 2021 Model Predictive Control (MPC) is a well-established advanced process control technology. There are many successful implementations of different predictive strategies in process industry.
Pawel D. Domanski, Maciej Lawrynczuk
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Robust regression via mutivariate regression depth [PDF]
Bernoulli, 2020 This paper studies robust regression in the settings of Huber's $ $-contamination models. We consider estimators that are maximizers of multivariate regression depth functions. These estimators are shown to achieve minimax rates in the settings of $ $-contamination models for various regression problems including nonparametric regression, sparse ...
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Huber Regression Analysis with a Semi-Supervised Method
Mathematics, 2022 In this paper, we study the regularized Huber regression algorithm in a reproducing kernel Hilbert space (RKHS), which is applicable to both fully supervised and semi-supervised learning schemes.
Yue Wang +4 more
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Robust Dynamic Mode Decomposition
IEEE Access, 2022 This paper develops a robust dynamic mode decomposition (RDMD) method endowed with statistical and numerical robustness. Statistical robustness ensures estimation efficiency at the Gaussian and non-Gaussian probability distributions, including heavy ...
Amir Hossein Abolmasoumi +2 more
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Mandatory IFRS Adoption and Real/Accruals Bases Earnings Management in the UK [PDF]
ACRN Journal of Finance and Risk Perspectives, 2021 Here, the link between the mandatory adoption of International Financial Reporting Standards (IFRS) and Real Earnings Management (REM), as well as Accrual Earnings Management (AEM), will be examined for non-financial listed firms in the London Stock ...
Mohammad I. Almaharmeh +2 more
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RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression
Journal of Statistical Software, 2016 An important task in astroparticle physics is the detection of periodicities in irregularly sampled time series, called light curves. The classic Fourier periodogram cannot deal with irregular sampling and with the measurement accuracies that are ...
Anita M. Thieler +2 more
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