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Robustness of Deepest Regression
Journal of Multivariate Analysis, 2000 The notion of the regression depth is one of the most interesting and important notions recently studied in multivariate analysis. The authors investigate the robustness properties of deepest regressions. It is shown that the deepest regression functional is Fisher-consistent for the conditional median, and has a breakdown value of 1/3 in all ...
Van Aelst, Stefan, Rousseeuw, Peter
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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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CoRR, 2016 Modern technologies are producing datasets with complex intrinsic structures, and they can be naturally represented as matrices instead of vectors. To preserve the latent data structures during processing, modern regression approaches incorporate the low-rank property to the model and achieve satisfactory performance for certain applications.
Hang Zhang 0010, Fengyuan Zhu, Shixin Li
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Estimating a unit price for roads maintenance activities using exponential robust regression
Journal of Civil Engineering and Management, 2014 Good road maintenance schemes allow reducing costs and extending the service life of roads. There are several methods to plan these project maintenances but all of them require input information about maintenance costs, which can be very different ...
Cristóbal Moena, Alfredo Serpell
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The effect of calibration errors on the accuracy of the eye movement recordings
Journal of Eye Movement Research, 2008 For calibrating eye movement recordings, a regression between spatially defined calibration points and corresponding measured raw data is performed.
Jörg Hoormann +2 more
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Robust analyzes for longitudinal clinical trials with missing and non-normal continuous outcomes
Statistical Theory and Related Fields, 2023 Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures ...
Siyi Liu +4 more
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Understanding the determinants of happiness through Gallup World Poll
Journal of Family Medicine and Primary Care, 2020 Background: The idea of happiness is as old as civilization, but breakthrough is achieved only in 20th century. Happiness can be broadly segmented into biological and behavioural component. The sufferings from illnesses hamper happiness.
Vidushi Jaswal +4 more
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MODIFIED FUZZY-ROBUST RIDGE REGRESSION FOR MULTICOLLINEAR, OUTLIER-CONTAMINATED DATA
Science Journal of University of ZakhoMulticollinearity is known to have a significant impact on the stability of linear regression parameter estimation, while the presence of outliers tends to compound this problem. Ridge regression helps to improve the multicollinearity problem, but it is
Vaman M Salih, Shelan S Ismaeel
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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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