Variable selection of varying dispersion student-\(t\) regression models

DOI10.1007/S11424-014-2223-9zbMath1320.93080OpenAlexW2010677940MaRDI QIDQ498090

Weihua Zhao, Ri-quan Zhang

Publication date: 25 September 2015

Published in: Journal of Systems Science and Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11424-014-2223-9

zbMATH Keywords

variable selectionLASSOSCADstudent-\(t\) distributionvarying dispersion

Mathematics Subject Classification ID

Estimation and detection in stochastic control theory (93E10) Stochastic systems in control theory (general) (93E03) Statistical distribution theory (62E99)

Related Items (6)

Variable Selection in Heteroscedastic Regression Models Under General Skew-t Distributional Models Using Information Complexity ⋮ Estimation and variable selection for mixture of joint mean and variance models ⋮ Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions ⋮ Penalized \(M\)-estimation based on standard error adjusted adaptive elastic-net ⋮ Doubly reweighted estimators for the parameters of the multivariate t-distribution ⋮ Variable selection for skew-normal mixture of joint location and scale models

Uses Software

• alr3
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Cites Work

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• Nearly unbiased variable selection under minimax concave penalty
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• Variable selection for semiparametric varying coefficient partially linear models
• Heteroscedasticity diagnostics for \(t\) linear regression models
• One-step sparse estimates in nonconcave penalized likelihood models
• Better Subset Regression Using the Nonnegative Garrote
• Joint modelling of location and scale parameters of the t distribution
• Robustness of the student t based M-estimator
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Variable selection in joint location and scale models of the skew-normal distribution
• Model Selection and Estimation in Regression with Grouped Variables
• Tuning parameter selectors for the smoothly clipped absolute deviation method

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