Results 21 to 30 of about 674 (121)
Bayesian approaches to shrinkage and sparse estimation [PDF]
, 2021 In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where high-dimensional and nonlinear/nonparametric inference is the norm in ...
Korobilis, Dimitris, Shimizu, Kenichi
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Rank theory approach to ridge, LASSO, preliminary test and Stein-type estimators: Comparative study [PDF]
, 2018 summary:In the development of efficient predictive models, the key is to identify suitable predictors for a given linear model. For the first time, this paper provides a comparative study of ridge regression, LASSO, preliminary test and Stein-type ...
Saleh, A. K. Md. Ehsanes +1 more
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High‐dimensional regression coefficient estimation by nuclear norm plus l1 norm penalization [PDF]
, 2023 We propose a new estimator of the regression coefficients for a high-dimensional linear regression model, which is de rived by replacing the sample predictor covariance matrix in the OLS estimator with a different predictor covariance matrix estimate ...
Matteo Farnè +3 more
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Regularization and Model Selection with Categorial Predictors and Effect Modifiers in Generalized Linear Models [PDF]
, 2012 We consider varying-coefficient models with categorial effect modifiers in the framework of generalized linear models. We distinguish between nominal and ordinal effect modifiers, and propose adequate Lasso-type regularization techniques that allow for ...
Gertheiss, Jan +2 more
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Prior elicitation and variable selection for bayesian quantile regression [PDF]
, 2013 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Bayesian subset selection suffers from three important difficulties: assigning priors over model space, assigning priors to all components of the regression
Al-Hamzawi, Rahim Jabbar Thaher
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Adaptive LASSO in High-dimensions
, 2022 The Least Absolute Shrinkage and Selector operator (LASSO) is a famous method for estimation and predictor selection simultaneously. But in certain situations where the LASSO is not consistent for predictor selection, Zou (2006).
Abdul Wahid
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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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Journal of MathematicsThe accurate estimation of correlation matrices is a foundational challenge in high-dimensional statistics. The sample correlation matrix, while unbiased, suffers from high variance when the number of variables p is large relative to the sample size n ...
Muath Awadalla, Yücel Tandoğdu
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, 2021 Forest information is requested at many levels and for many purposes. Sampling-based national forest inventories (NFIs) can provide reliable estimates on national and regional levels.
Mats Nilsson (7964) +1 more
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New Aspects on the Modified Group LASSO using the Least Angle Regression and Shrinkage Algorithm
, 2021 In this paper, we propose a new method which is a modified group lasso with least angle regression selection to improve the high dimensional linear model in explanatory data.
A. El Sheikh, Ahmed +2 more
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