Mixtures of regression models with incomplete and noisy data

Byoung Cheol Jung, Sooyoung Cheon, Hwa Kyung Lim

Research output: Contribution to journalArticlepeer-review


The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.

Original languageEnglish
Pages (from-to)444-463
Number of pages20
JournalCommunications in Statistics Part B: Simulation and Computation
Issue number2
StatePublished - 7 Feb 2018


  • EM algorithm
  • Maximum likelihood
  • Missing values
  • Mixtures of regression models
  • Outliers


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