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

Kento Nakamura, Keisuke Yano and Fumiyasu Komaki

Computational Statistics & Data Analysis, 2020, vol. 145, issue C

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.

Keywords: Alternating Direction Method of Multipliers; Expectation–Maximization algorithms; Kendall distances; Mallows models (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:145:y:2020:i:c:s0167947319302609

DOI: 10.1016/j.csda.2019.106905

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