Adaptive robust regression with continuous Gaussian scale mixture errors

DOI10.1016/j.jkss.2016.08.002zbMath1357.62246OpenAlexW2520106276MaRDI QIDQ508114

Taewook Lee, Young Joo Yoon, Jungsik Noh, Byungtae Seo

Publication date: 9 February 2017

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jkss.2016.08.002

zbMATH Keywords

asymptoticsM-estimationadaptive robust regressioncontinuous scale Gaussian mixture

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (4)

Semiparametric estimation for linear regression with symmetric errors ⋮ Semiparametric mixture: continuous scale mixture approach ⋮ Accelerated failure time modeling via nonparametric mixtures ⋮ On \(p\)-generalized elliptical random processes

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Multivariate linear regression with non-normal errors: a solution based on mixture models
• The use of mixtures for dealing with non-normal regression errors
• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• Estimating a real parameter in a class of semiparametric models
• How broad is the class of normal scale mixtures?
• Efficient maximum likelihood estimation in semiparametric mixture models
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities.
• Weak convergence and empirical processes. With applications to statistics
• Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters
• On scale mixtures of normal distributions
• High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale
• Asymptotic behavior of general M-estimates for regression and scale with random carriers
• Efficient Bounded-Influence Regression Estimation
• An Algorithm for Computing the Nonparametric MLE of a Mixing Distribution
• Generalized S-Estimators
• Computation of High Breakdown Nonlinear Regression Parameters
• The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data
• On Fast Computation of the Non-Parametric Maximum Likelihood Estimate of a Mixing Distribution
• Identifiability of Finite Mixtures of Elliptical Distributions
• Robust Estimation of a Location Parameter
• Infinite Divisibility and Variance Mixtures of the Normal Distribution
• Consistent Estimates Based on Partially Consistent Observations

This page was built for publication: Adaptive robust regression with continuous Gaussian scale mixture errors