Variable Selection in Heteroscedastic Regression Models Under General Skew-t Distributional Models Using Information Complexity
DOI10.1007/978-3-030-42196-0_4OpenAlexW3048474246MaRDI QIDQ5050405
Hamparsum Bozdogan, Olcay Arslan, Yeşim Güney
Publication date: 15 November 2022
Published in: Emerging Topics in Statistics and Biostatistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-030-42196-0_4
robustnessmodel selectionregressionskewnessvariable selectionstatistical modelingskew \(t\)-distributionmaximum likelihood (ML)model complexitypenalized estimationskew distribution\(t\)-distributionjoint locationlog-linear model for the scalemaximal covariance complexityobserved Fisher information matrixscale and skewness model (JLSSM)
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- Variable selection of varying dispersion student-\(t\) regression models
- Simultaneous variable selection for heteroscedastic regression models
- Bayesian inference in joint modelling of location and scale parameters of the \(t\) distribution for longitudinal data
- Variable selection for joint mean and dispersion models of the inverse Gaussian distribution
- Informational complexity criteria for regression models.
- Variable selection in joint location, scale and skewness models with a skew-\(t\)-normal distribution
- Akaike's information criterion and recent developments in information complexity
- Joint modeling of location and scale parameters of the skew-normal distribution
- A robust approach to joint modeling of mean and scale covariance for longitudinal data
- Variable selection in joint location, scale and skewness models of the skew-normal distribution
- A Robust Variable Selection tot-type Joint Generalized Linear Models via Penalizedt-type Pseudo-likelihood
- On the information-based measure of covariance complexity and its application to the evaluation of multivariate linear models
- Joint modelling of location and scale parameters of the t distribution
- Estimating Regression Models with Multiplicative Heteroscedasticity
- The multivariate skew-normal distribution
- Robust Location and Scale Estimation Based on the Univariate Generalizedt(GT) Distribution
- Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skewt-Distribution
- Variable selection in joint mean and variance models of Box–Cox transformation
- Variable selection in joint location and scale models of the skew-normal distribution
- Variable Selection in Joint Location and Scale Models of the Skew-t-Normal Distribution
- The Skew-normal Distribution and Related Multivariate Families*
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