Variable Selection in Joint Location and Scale Models of the Skew-t-Normal Distribution

DOI10.1080/03610918.2012.712182zbMath1291.62062OpenAlexW2193568230MaRDI QIDQ5415874

Liu-Cang Wu

Publication date: 19 May 2014

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.712182

zbMATH Keywords

variable selectionpenalized maximum likelihood estimatorheteroscedastic regression modelsjoint location and scale models of the skew-\(t\)-normal distributionskew-\(t\)-normal distribution (StN)

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12)

Related Items (9)

Variable selection in finite mixture of median regression models using skew-normal distribution ⋮ Variable Selection in Heteroscedastic Regression Models Under General Skew-t Distributional Models Using Information Complexity ⋮ Robust variable selection in finite mixture of regression models using the t distribution ⋮ Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions ⋮ Bayesian inference for joint location and scale nonlinear models with skew-normal errors ⋮ A general location model with zero-inflated counts and skew normal outcomes ⋮ Shape mixtures of skew-\(t\)-normal distributions: characterizations and estimation ⋮ Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution ⋮ A general class of scale-shape mixtures of skew-normal distributions: properties and estimation

Cites Work

• Heteroscedastic symmetrical linear models
• A nonlinear regression model with skew-normal errors
• Multivariate elliptical models with general parameterization
• Simultaneous variable selection for heteroscedastic regression models
• Heteroscedasticity diagnostics for \(t\) linear regression models
• Maximum likelihood inference for mixtures of skew Student-\(t\)-normal distributions through practical EM-type algorithms
• Analyzing skewed data by power normal model
• Diagnostics for skew-normal nonlinear regression models with AR(1) errors
• Homogeneity diagnostics for skew-normal nonlinear regression models
• Bayesian density estimation using skew Student-\(t\)-normal mixtures
• Skewed multivariate models related to hidden truncation and/or selective reporting. With discussion and a rejoinder by the authors.
• Variable selection in semiparametric regression modeling
• Bartlett corrections in heteroskedastic \(t\) regression models
• Joint modelling of location and scale parameters of the t distribution
• Statistical Diagnostics for Skew-t-Normal Nonlinear Models
• Robustness of the student t based M-estimator
• Statistical Applications of the Multivariate Skew Normal Distribution
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skewt-Distribution
• GOODNESS-OF-FIT TESTS FOR THE SKEW-NORMAL DISTRIBUTION
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• The Skew-normal Distribution and Related Multivariate Families*

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