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Regularized Ordinal Regression and the ordinalNet R Package
Journal of Statistical Software, 2021 Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection.
Michael J. Wurm +2 more
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Canadian Journal of Statistics, 2015 AbstractWe propose a new sparse regression method called thecomponent lasso, based on a simple idea. The method uses the connectedâcomponents structure of the sample covariance matrix to split the problem into smaller ones. It then applies the lasso to each subproblem separately, obtaining a coefficient vector for each one.
Hussami, Nadine, Tibshirani, Robert J.
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An Application of High-Dimensional Statistics to Predictive Modeling of Grade Variability
Geosciences, 2020 The economic viability of a mining project depends on its efficient exploration, which requires a prediction of worthwhile ore in a mine deposit. In this work, we apply the so-called LASSO methodology to estimate mineral concentration within unexplored ...
Juri Hinz +2 more
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Computational Statistics & Data Analysis, 2014 19 pages, 4 figures and 7 ...
Bergersen, Linn Cecilie +2 more
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Salvage decision-making based on carbon following an eastern spruce budworm outbreak
Frontiers in Forests and Global Change, 2023 Forest disturbances, such as an eastern spruce budworm (Choristoneura fumiferana) outbreak, impact the strength and persistence of forest carbon sinks. Salvage harvests are a typical management response to widespread tree mortality, but the decision to ...
Lisa N. Scott +8 more
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Networked Exponential Families for Big Data Over Networks
IEEE Access, 2020 The data generated in many application domains can be modeled as big data over networks, i.e., massive collections of high-dimensional local datasets related via an intrinsic network structure.
Alexander Jung
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Generalized Stochastic Restricted LARS Algorithm
Ruhuna Journal of Science, 2022 The Least Absolute Shrinkage and Selection Operator (LASSO) is used to tackle both the multicollinearity issue and the variable selection concurrently in the linear regression model.
Manickavasagar Kayanan +1 more
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Penalized Lasso Methods in Health Data: application to trauma and influenza data of Kerman [PDF]
Journal of Kerman University of Medical Sciences, 2019 Background: Two main issues that challenge model building are number of Events Per Variable and multicollinearity among exploratory variables. Our aim is to review statistical methods that tackle these issues with emphasize on penalized Lasso regression ...
Abolfazl Hosseinnataj +6 more
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, 2011 In the last decade several estimators have been proposed that enforce the grouping property. A regularized estimate exhibits the grouping property if it selects groups of highly correlated predictor rather than selecting one representative.
Flexeder, Claudia +2 more
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Load Nowcasting: Predicting Actuals with Limited Data
Energies, 2020 We introduce the problem of load nowcasting to the energy forecasting literature. The recent load of the objective area is predicted based on limited available metering data within this area.
Florian Ziel
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