A generative model for rank data based on an insertion sorting algorithm

Biernacki, Christophe; Jacques, Julien

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1016/j.csda.2012.08.008

Title :

A generative model for rank data based on an insertion sorting algorithm

Author(s) :

Biernacki, Christophe [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Jacques, Julien [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]

Journal title :

Computational Statistics and Data Analysis

Pages :

162-176.

Publisher :

Elsevier

Publication date :

2013

ISSN :

0167-9473

HAL domain(s) :

Mathématiques [math]/Statistiques [math.ST]
Statistiques [stat]/Théorie [stat.TH]

English abstract : [en]

An original and meaningful probabilistic generative model for full rank data modelling is proposed. Rank data arise from a sorting mechanism which is generally unobservable for statisticians. Assuming that this process ...
Show more >An original and meaningful probabilistic generative model for full rank data modelling is proposed. Rank data arise from a sorting mechanism which is generally unobservable for statisticians. Assuming that this process relies on paired comparisons, the insertion sort algorithm is known as being the best candidate in order to minimize the number of potential paired misclassifications for a moderate number of objects to be ordered. Combining this optimality argument with a Bernoulli event during paired comparison step, a model that possesses desirable theoretical properties, among which are unimodality, symmetry and identifiability is obtained. Maximum likelihood estimation can also be performed easily through an EM or a SEM-Gibbs algorithm (depending on the number of objects to be ordered) by involving the latent initial presentation order of the objects. Finally, the practical relevance of the proposal is illustrated through its adequacy with several real data sets and a comparison with a standard rank data model.Show less >

Language :

Anglais

Popular science :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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