Variable selection for joint mean and dispersion models of the inverse Gaussian distribution

DOI10.1007/s00184-011-0352-xzbMath1410.62132OpenAlexW1993513030MaRDI QIDQ715499

Hui-Qiong Li, Liu-Cang Wu

Publication date: 29 October 2012

Published in: Metrika (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00184-011-0352-x

zbMATH Keywords

variable selectionLassoSCADpenalized maximum likelihoodjoint mean and dispersion models of the inverse Gaussian distribution

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic properties of nonparametric inference (62G20)

Related Items (21)

Variable Selection in Heteroscedastic Regression Models Under General Skew-t Distributional Models Using Information Complexity ⋮ Joint estimation and variable selection for mean and dispersion in proper dispersion models ⋮ Variable selection in joint location, scale and skewness models of the skew-normal distribution ⋮ Estimation and variable selection for mixture of joint mean and variance models ⋮ Principal component ridge type estimator for the inverse Gaussian regression model ⋮ A semiparametric Bayesian approach to joint mean and variance models ⋮ Robust rank-based variable selection in double generalized linear models with diverging number of parameters under adaptive Lasso ⋮ Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions ⋮ Variable selection in finite mixture of location and mean regression models using skew-normal distribution ⋮ Modified see variable selection for linear instrumental variable regression models ⋮ Bayesian inference for joint location and scale nonlinear models with skew-normal errors ⋮ Model selection criteria in beta regression with varying dispersion ⋮ On variable selection in joint modeling of mean and dispersion ⋮ Variable selection in joint mean and variance models of Box–Cox transformation ⋮ Variable selection for varying dispersion beta regression model ⋮ Variable selection of varying dispersion student-\(t\) regression models ⋮ Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution ⋮ A Robust Variable Selection tot-type Joint Generalized Linear Models via Penalizedt-type Pseudo-likelihood ⋮ Variable selection for skew-normal mixture of joint location and scale models ⋮ Performance of ridge estimator in inverse Gaussian regression model ⋮ Joint modeling of location and scale parameters of the skew-normal distribution

Uses Software

• GLIM

Cites Work

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• Diagnostics for heteroscedasticity in regression
• Variable selection for semiparametric varying coefficient partially linear errors-in-variables models
• The inverse Gaussian distribution. Statistical theory and applications
• Variable selection in semiparametric regression modeling
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Joint modelling of location and scale parameters of the t distribution
• Varying Dispersion Diagnostics for Inverse Gaussian Regression Models
• Estimating Regression Models with Multiplicative Heteroscedasticity
• Generalized linear models for the analysis of quality-improvement experiments
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Tuning parameter selectors for the smoothly clipped absolute deviation method

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