Data wrangling, computational burden, automation, robustness and accuracy in ecological inference forecasting of R×C tables
Pavía, Jose M. (Universitat de València)
Romero, Rafael (Universitat Politècnica de València)

Data:	2023
Resum:	This paper assesses the two current major alternatives for ecological inference, based on a multinomial-Dirichlet Bayesian model and on mathematical programming. Their performance is evaluated in a database made up of almost 2000 real datasets for which the actual cross-distributions are known. The analysis reveals both approaches as complementarity, each one of them performing better in a different area of the simplex space, although with Bayesian solutions deteriorating when the amount of information is scarce. After offering some guidelines regarding the appropriate contexts for employing each one of the algorithms, we conclude with some ideas for exploiting their complementarities.
Nota:	Acknowledgments. The authors wish to thank Carolina Plescia for providing us with the electoral Scottish data handled in this paper and two anonymous reviewers and the editors for their really valuable comments and suggestions. We are grateful to M. Hodkinson for translating and revising the English of the paper and Priscila Espinosa for her tips about LATEX. This research has been supported by Conseller'ıa d'Innovacio, Universitats, Ciència i Societat Digital, Generalitat Valenciana [grant number AICO/2021/257] and by the Ministerio de Economía e Innovacion [grant number PID2021-128228NB-I00].
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Ecological inference ; Voter transitions ; US voting rights ; Two-way contingency tables ; Ei.MD.bayes ; Lphom ; R-packages
Publicat a:	SORT : statistics and operations research transactions, Vol. 47 Núm. 1 (2023) , p. 151-186, ISSN 2013-8830

Adreça original: https://raco.cat/index.php/SORT/article/view/416953
DOI: 10.57645/20.8080.02.4

36 p, 1.2 MB

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