Robust mixture regression modeling based on the normal mean-variance mixture distributions

Mehrdad Naderi, Elham Mirfarah, Wan Lun Wang, Tsung I. Lin

研究成果: Article同行評審

4 引文 斯高帕斯(Scopus)


Mixture regression models (MRMs) are widely used to capture the heterogeneity of relationships between the response variable and one or more predictors coming from several non-homogeneous groups. Since the conventional MRMs are quite sensitive to departures from normality caused by extra skewness and possible heavy tails, various extensions built on more flexible distributions have been put forward in the last decade. The class of normal mean-variance mixture (NMVM) distributions that arise from scaling both the mean and variance of a normal random variable with a common mixing distribution encompasses many prominent (symmetric or asymmetrical) distributions as special cases. A unified approach to robustifying MRMs is proposed by considering the class of NMVM distributions for component errors. An expectation conditional maximization either (ECME) algorithm, which incorporates membership indicators and the latent scaling variables as the missing data, is developed for carrying out maximum likelihood (ML) estimation of model parameters. Four simulation studies are conducted to examine the finite-sample property of ML estimators and the robustness of the proposed model against outliers for contaminated and noisy data. The usefulness and superiority of our methodology are demonstrated through applications to two real datasets.

期刊Computational Statistics and Data Analysis
出版狀態Published - 2023 4月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 計算數學
  • 計算機理論與數學
  • 應用數學


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