Fit and analysis of finite Mixtures of Mallows models with Spearman Distance for full and partial rankings with arbitrary missing positions. Inference is conducted within the maximum likelihood framework via Expectation-Maximization algorithms. Estimation uncertainty is tackled via diverse versions of bootstrapping as well as via Hessian-based standard errors calculations. The most relevant reference of the methods is Crispino, Mollica, Astuti and Tardella (2023) .
MSmix: Finite Mixtures of Mallows Models with Spearman Distance for Full and Partial Rankings / Mollica, Cristina; Crispino, Marta; Modugno, Lucia; Tardella, Luca. - (2024).
MSmix: Finite Mixtures of Mallows Models with Spearman Distance for Full and Partial Rankings
Cristina Mollica
;Luca Tardella
2024
Abstract
Fit and analysis of finite Mixtures of Mallows models with Spearman Distance for full and partial rankings with arbitrary missing positions. Inference is conducted within the maximum likelihood framework via Expectation-Maximization algorithms. Estimation uncertainty is tackled via diverse versions of bootstrapping as well as via Hessian-based standard errors calculations. The most relevant reference of the methods is Crispino, Mollica, Astuti and Tardella (2023) .File allegati a questo prodotto
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