Estimation and variable selection for single-index models with non ignorable missing data

Abstract This article investigates the estimation and variable selection procedures of single-index models when the data have non ignorable response. For estimation of the index coefficients, a new procedure based on martingale difference divergence is developed. Consistency and asymptotic normality of the proposed estimators are established. The finite sample performance of the proposed procedure is assessed through extensive simulation studies. Finally, we illustrate the proposed method through a real body fat data set.

• Publication year

  2023

• Venue

  Communications in Statistics - Theory and Methods

• Publication date

  2023-04-12

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1080/03610926.2023.2198625
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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