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

DOI10.1016/j.csda.2019.106905OpenAlexW2997651755WikidataQ126410832 ScholiaQ126410832MaRDI QIDQ2305301

Kento Nakamura, Keisuke Yano, Fumiyasu Komaki

Publication date: 10 March 2020

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2019.106905

zbMATH Keywords

Mallows modelsalternating direction method of multipliersexpectation-maximization algorithmsKendall distances

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes
• Probability models and statistical analyses for ranking data. Papers presented at the AMS-IMS-SIAM conference, Amherst, MA, USA, June 1990
• A generalization of spectral analysis with application to ranked data
• An Akaike information criterion for model selection in the presence of incomplete data.
• Model-based clustering for multivariate partial ranking data
• NON-NULL RANKING MODELS. I
• Learning to Rank for Information Retrieval
• Statistical Analysis with Missing Data, Third Edition
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