Results 21 to 30 of about 103,730 (259)
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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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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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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Journal of Computational and Graphical Statistics, 2019 We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of some prespecified modifying variables. These modifiers might be variables such as gender, age, or time. The paradigm is quite general, with each lasso coefficient modified by a sparse linear function of the modifying variables Z.
Tibshirani, Robert, Friedman, Jerome
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Network Inference with the Lasso
Multivariate Behavioral Research, 2022 Calculating confidence intervals and p-values of edges in networks is useful to decide their presence or absence and it is a natural way to quantify uncertainty. Since Lasso estimation is often used to obtain edges in a network, and the underlying distribution of Lasso estimates is discontinuous and has probability one at zero when the estimate is zero,
Lourens Waldorp, Jonas Haslbeck
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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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CoRR, 2020 In this paper, we revisit the regret minimization problem in sparse stochastic contextual linear bandits, where feature vectors may be of large dimension $d$, but where the reward function depends on a few, say $s_0\ll d$, of these features only.
Kaito Ariu +2 more
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The Annals of Applied Statistics, 2011 We propose a computationally intensive method, the random lasso method, for variable selection in linear models. The method consists of two major steps. In step 1, the lasso method is applied to many bootstrap samples, each using a set of randomly selected covariates. A measure of importance is yielded from this step for each covariate.
Wang, Sijian +3 more
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Robust Regression and Lasso [PDF]
IEEE Transactions on Information Theory, 2010 Lasso, or $\ell^1$ regularized least squares, has been explored extensively for its remarkable sparsity properties. It is shown in this paper that the solution to Lasso, in addition to its sparsity, has robustness properties: it is the solution to a robust optimization problem. This has two important consequences.
Huan Xu 0001 +2 more
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