Variable Selection Using a Smooth Information Criterion for Distributional Regression Models
type: Item
instance of: scholarly article
publication date: 2021-10-06T00:00:00Z
publication date: 2023-07-20T00:00:00Z
arXiv ID: 2110.02643
arXiv classification: stat.ME
Mathematics Subject Classification ID: 62J07
Mathematics Subject Classification ID: 62-08
author: Meadhbh O'Neill
author: Kevin Burke
MaRDI profile type: Publication
title: Variable selection using a smooth information criterion for distributional regression models
zbMATH Open document ID: 1517.62040
published in: Statistics and Computing
full work available at URL: https://arxiv.org/abs/2110.02643
zbMATH DE Number: 7714834
zbMATH Keywords: variable selection
zbMATH Keywords: information criteria
zbMATH Keywords: penalized maximum likelihood
zbMATH Keywords: heteroscedasticity
zbMATH Keywords: distributional regression
zbMATH Keywords: multiparameter regression
OpenAlex ID: W4366690028
cites work: A new look at the statistical model identification
cites work: Checking for Lack of Fit in Linear Models with Parametric Variance Functions
cites work: Multi‐parameter regression survival modeling: An alternative to proportional hazards
cites work: Q4725475
cites work: Sparse regression with multi-type regularized feature modeling
cites work: Least angle regression. (With discussion)
cites work: Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
cites work: Variable selection for Cox's proportional hazards model and frailty model
cites work: A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
cites work: Adaptive robust variable selection
cites work: Pathwise coordinate optimization
cites work: Lasso-type penalization in the framework of generalized additive models for location, scale and shape
cites work: Hedonic housing prices and the demand for clean air
cites work: Estimating Regression Models with Multiplicative Heteroscedasticity
cites work: Variable selection using MM algorithms
cites work: A globally convergent algorithm for Lasso-penalized mixture of linear regression models
cites work: Robust Statistics
cites work: A uniform framework for the combination of penalties in generalized structured models
cites work: A study of error variance estimation in Lasso regression
cites work: Generalized Additive Models for Location, Scale and Shape
cites work: Estimating the dimension of a model
cites work: Q4344404
cites work: GAMLSS: A distributional regression approach
cites work: Sparse Estimation of Generalized Linear Models (GLM) via Approximated Information Criteria
cites work: Q4864293
cites work: BAMLSS: Bayesian Additive Models for Location, Scale, and Shape (and Beyond)
cites work: Unified LASSO Estimation by Least Squares Approximation
cites work: Shrinkage tuning parameter selection with a diverging number of parameters
cites work: Q3311506
cites work: Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation
cites work: The Adaptive Lasso and Its Oracle Properties
cites work: Regularization and Variable Selection Via the Elastic Net
DOI: 10.48550/ARXIV.2110.02643
DOI: 10.1007/S11222-023-10204-8
description: scientific article from arXiv