Results 11 to 20 of about 4,362,963 (201)
Regularized and robust regression methods for high dimensional data [PDF]
, 2014 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Recently, variable selection in high-dimensional data has attracted much research interest.
Hashem, Hussein Abdulahman
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Robust Regression in Stata [PDF]
SSRN Electronic Journal, 2008 In regression analysis, the presence of outliers in the dataset can strongly distort the classical least-squares estimator and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the statistical literature. In Stata, some of these methods are available through the rreg and qreg commands. Unfortunately,
Verardi, Vincenzo, Croux, Christophe
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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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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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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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Cellwise robust M regression [PDF]
Computational Statistics & Data Analysis, 2020 The cellwise robust M regression estimator is introduced as the first estimator of its kind that intrinsically yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points.
Peter Filzmoser +4 more
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Robust regression with compositional covariates
Computational Statistics & Data Analysis, 2022 43 pages, 12 ...
Aditya Mishra, Christian L. Müller
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fsdaSAS: A Package for Robust Regression for Very Large Datasets Including the Batch Forward Search
Stats, 2021 The forward search (FS) is a general method of robust data fitting that moves smoothly from very robust to maximum likelihood estimation. The regression procedures are included in the MATLAB toolbox FSDA.
Francesca Torti +2 more
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Robust Multivariate Regression
Technometrics, 2004 We introduce a robust method for multivariate regression based on robust estimation of the joint location and scatter matrix of the explanatory and response variables. As a robust estimator of location and scatter, we use the minimum covariance determinant (MCD) estimator of Rousseeuw.
Rousseeuw, Peter +3 more
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Robust regression with imprecise data [PDF]
, 2011 We consider the problem of regression analysis with imprecise data. By imprecise data we mean imprecise observations of precise quantities in the form of sets of values.
Wiencierz, Andrea +1 more
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