A computationally efficient sequential regression imputation algorithm for multilevel data

Akkaya Hocagil T., Yucel R. M.

Journal of Applied Statistics, vol.51, no.11, pp.2258-2278, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 51 Issue: 11
  • Publication Date: 2024
  • Doi Number: 10.1080/02664763.2023.2277669
  • Journal Name: Journal of Applied Statistics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Computer & Applied Sciences, Veterinary Science Database, zbMATH
  • Page Numbers: pp.2258-2278
  • Keywords: computational efficiency, fast variable by variable imputation, multilevel data, multiple imputation by chained equations, Sequential regression imputation
  • Ankara University Affiliated: No

Abstract

Due to the computational burden, especially in high-dimensional settings, sequential imputation may not be practical. In this paper, we adopt computationally advantageous methods by sampling the missing data from their perspective predictive distributions, which leads to significantly improved computation time in the class of variable-by-variable imputation algorithms. We assess the computational performance in a comprehensive simulation study. We then compare and contrast the performance of our algorithm with commonly used alternatives. The results show that our method has a significant advantage over the commonly used alternatives with respect to computational efficiency and inferential quality. Finally, we demonstrate our methods in a substantive problem aimed at investigating the effects of area-level behavioral, socioeconomic, and demographic characteristics on poor birth outcomes in New York State among singleton births.