Data wrangling, computational burden, automation, robustness and accuracy in ecological inference forecasting of R×C tables

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Data wrangling, computational burden, automation, robustness and accuracy in ecological inference forecasting of R×C tables - RP:5240

Handle:

https://hdl.handle.net/20.500.11797/RP5240

Autor según el artículo:	Romero, Rafael Pavía, Jose M.
Año de publicación de la revista:	2023
Tipo de publicación:	##rt.metadata.pkp.peerReviewed## info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article
Resumen:	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.
Palabras clave:	ecological inference

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DocumentPrincipal	DocumentPrincipal	application/pdf

Descripción:	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.
Tipo:	SORT:ART ##rt.metadata.pkp.peerReviewed## info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article
Títol:	Data wrangling, computational burden, automation, robustness and accuracy in ecological inference forecasting of R×C tables
Materia:	ecological inference
Formato:	application/zip application/pdf
Autor:	Romero, Rafael Pavía, Jose M.
Fecha:	2023
Editor:	Universitat Rovira i Virgili Institut d'Estadística de Catalunya Universitat de Lleida Universitat de Girona Universitat Pompeu Fabra Universitat Politècnica de Catalunya Universitat Autònoma de Barcelona Universitat de Barcelona

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