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

Pertany a la col·lecció RP:SerieSORT

TÍTOL:

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 segons l'article:	Romero, Rafael Pavía, Jose M.
Any de publicació de la revista:	2023
Tipus de publicació:	##rt.metadata.pkp.peerReviewed## info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article
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.
Paraules clau:	ecological inference

Cerca el teu registre a:

Fitxers disponibles
Fitxer	Descripció	Format
DocumentPrincipal	DocumentPrincipal	application/pdf

Descripció:	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.
Tipus:	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
Matèria:	ecological inference
Format:	application/zip application/pdf
Autor:	Romero, Rafael Pavía, Jose M.
Data:	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

Informació