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Adjacency-based regularization for partially ranked data with non-ignorable missing

Author

Listed:
  • Nakamura, Kento
  • Yano, Keisuke
  • Komaki, Fumiyasu

Abstract

In analyzing ranked data, we often encounter situations in which data are partially ranked. Regarding partially ranked data as missing data, this paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing mechanism. We propose estimators for both complete rankings and missing mechanisms together with a simple estimation procedure. The proposed procedure leverages the structured regularization based on an adjacency structure behind partially ranked data as well as the Expectation–Maximization algorithm. The experimental results demonstrate that the proposed estimator works well under non-ignorable missing mechanisms.

Suggested Citation

  • Nakamura, Kento & Yano, Keisuke & Komaki, Fumiyasu, 2020. "Adjacency-based regularization for partially ranked data with non-ignorable missing," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:csdana:v:145:y:2020:i:c:s0167947319302609
    DOI: 10.1016/j.csda.2019.106905
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