Fitting a mixture distribution to a variable subject to heteroscedastic measurement errors

DOI10.1007/s001800300129zbMath1037.62009OpenAlexW1768449501MaRDI QIDQ1424624

Markus Thamerus

Publication date: 16 March 2004

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s001800300129

zbMATH Keywords

information matrixEM algorithmmaximum likelihood estimatorfinite mixture distribution

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Point estimation (62F10)

Related Items (2)

Some recent advances in measurement error models and methods ⋮ Density deconvolution in a two-level heteroscedastic model with unknown error density

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family
• Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• Estimating Mixtures of Normal Distributions and Switching Regressions
• Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model
• Maximum likelihood estimation of a mixture of normal regressions: starting values and singularities
• Practical Bayesian Density Estimation Using Mixtures of Normals
• On the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture with Unequal Variances

This page was built for publication: Fitting a mixture distribution to a variable subject to heteroscedastic measurement errors