Multivariate Contaminated Normal Censored Regression Model: Properties and Maximum Likelihood Inference
From MaRDI portal
Publication:6141212
DOI10.1080/10618600.2023.2184375OpenAlexW4321782410MaRDI QIDQ6141212
Publication date: 22 January 2024
Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10618600.2023.2184375
censored dataECM algorithmexpected information matrixmild outlierstruncated multivariate contaminated normal distribution
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Linear censored regression models with scale mixtures of normal distributions
- Modern mathematical tools and techniques in capturing complexity
- Pseudo-likelihood estimation of multivariate normal parameters in the presence of left-censored data
- Least absolute deviations estimation for the censored regression model
- Estimating the dimension of a model
- Nonlinear censored regression models with heavy-tailed distributions
- Finite mixture modeling of censored data using the multivariate Student-\(t\) distribution
- Mixtures of factor analyzers with covariates for modeling multiply censored dependent variables
- Multivariate hidden Markov regression models: random covariates and heavy-tailed distributions
- Robust clustering of multiply censored data via mixtures of \(t\) factor analyzers
- Mixtures of multivariate contaminated normal regression models
- Bayesian variable selection in linear regression models with non-normal errors
- Model-based clustering of censored data via mixtures of factor analyzers
- Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model
- The multivariate skew-slash distribution
- Parsimonious mixtures of multivariate contaminated normal distributions
- Maximum likelihood estimation via the ECM algorithm: A general framework
- ON THE SOLUTION OF ESTIMATING EQUATIONS FOR TRUNCATED AND CENSORED SAMPLES FROM NORMAL POPULATIONS
- Mixture Models, Outliers, and the EM Algorithm
- The multivariate leptokurtic‐normal distribution and its application in model‐based clustering
- The multivariate tail-inflated normal distribution and its application in finance
- Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions
- Allometric analysis using the multivariate shifted exponential normal distribution
- Multivariate linear mixed models with censored and nonignorable missing outcomes, with application to AIDS studies
This page was built for publication: Multivariate Contaminated Normal Censored Regression Model: Properties and Maximum Likelihood Inference