Variable selection in finite mixture of regression models using the skew-normal distribution

Variable selection in finite mixture of regression (FMR) models is frequently used in statistical modeling. The majority of applications of variable selection in FMR models use a normal distribution for regression error. Such assumptions are unsuitable for a set of data containing a group or groups of observations with asymmetric behavior. In this paper, we introduce a variable selection procedure for FMR models using the skew-normal distribution. With appropriate choice of the tuning parameters, we establish the theoretical properties of our procedure, including consistency in variable selection and the oracle property in estimation. To estimate the parameters of the model, a modified EM algorithm for numerical computations is developed. The methodology is illustrated through numerical experiments and a real data example.

• Publication year

  2019

• Venue

  Journal of Applied Statistics

• Publication date

  2019-12-31

• Fields of study

  Mathematics, Computer Science, Medicine

• Identifiers
  DOI 10.1080/02664763.2019.1709051PMID 35707710PMCID PMC9042060
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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