Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions
From MaRDI portal
Publication:5107518
DOI10.1080/00949655.2019.1658111OpenAlexW2970483984MaRDI QIDQ5107518
Fatma Zehra Dogru, Olcay Arslan, Ke-ming Yu
Publication date: 27 April 2020
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: http://bura.brunel.ac.uk/handle/2438/19014
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Robust mixture regression model fitting by Laplace distribution
- Variable selection of varying dispersion student-\(t\) 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
- Bayesian density estimation using skew Student-\(t\)-normal mixtures
- Estimating the dimension of a model
- Standard errors of fitted component means of normal mixture
- Identifiability of models for clusterwise linear regression
- Robust mixture regression using the \(t\)-distribution
- Variable selection in joint location, scale and skewness models with a skew-\(t\)-normal distribution
- Joint modeling of location and scale parameters of the skew-normal distribution
- A skew-normal mixture of joint location, scale and skewness models
- 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
- Robust clusterwise linear regression through trimming
- Robust Mixture Regression Using Mixture of Different Distributions
- Mixture Densities, Maximum Likelihood and the EM Algorithm
- Joint modelling of location and scale parameters of the t distribution
- On rates of convergence of efficient detection criteria in signal processing with white noise
- On Bayesian Modeling of Fat Tails and Skewness
- An Iterative Procedure for Obtaining Maximum-Likelihood Estimates of the Parameters for a Mixture of Normal Distributions
- Estimating Regression Models with Multiplicative Heteroscedasticity
- Estimating Mixtures of Normal Distributions and Switching Regressions
- Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model
- Statistical Applications of the Multivariate Skew Normal Distribution
- Mixed Poisson Regression Models with Covariate Dependent Rates
- A Generalized Linear Modeling Approach to Robust Design
- Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling
- Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skewt-Distribution
- The Consistency of Estimators in Finite Mixture Models
- Robust variable selection in finite mixture of regression models using the t distribution
- Robust mixture regression modeling using the least trimmed squares (LTS)-estimation method
- Robust mixture regression based on the skew t 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
- Bayesian Density Regression
- A New Approach to Estimating Switching Regressions
- Note on the Consistency of the Maximum Likelihood Estimate
This page was built for publication: Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions