Result: Data wrangling, computational burden, automation, robustness and accuracy in ecological inference forecasting of R×C tables
Title:
Data wrangling, computational burden, automation, robustness and accuracy in ecological inference forecasting of R×C tables
Authors:
Source:
Dipòsit Digital de Documents de la UAB
Universitat Autònoma de Barcelona
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Pavía Miralles, José Manuel Romero Villafranca, Rafael 2023 Data Wrangling, Computational Burden, Automation, Robustness and Accuracy in Ecological Inference Forecasting of R×C Tables Sort-Statistics And Operations Research Transactions 47 1 151 186
RODERIC. Repositorio Institucional de la Universitat de València
instname
Universitat Autònoma de Barcelona
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Pavía Miralles, José Manuel Romero Villafranca, Rafael 2023 Data Wrangling, Computational Burden, Automation, Robustness and Accuracy in Ecological Inference Forecasting of R×C Tables Sort-Statistics And Operations Research Transactions 47 1 151 186
RODERIC. Repositorio Institucional de la Universitat de València
instname
Publisher Information:
Institut d'Estadística de Catalunya, 2023.
Publication Year:
2023
Subject Terms:
Combinatorial analysis, ei.MD.bayes, 05 Combinatòria, Classificació AMS::05 Combinatorics::05A Enumerative combinatorics, mathematical programming::90C Mathematical programming, two-way contingency tables, Ei.MD.bayes, lphom, Classificació AMS::91 Game theory, economics, social and behavioral sciences, ecologia, Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica, voter transitions, Two-way contingency tables, R-packages, Game theory, ecological inference, 05A Problemes de combinatòria clàssica, Classificació AMS::90 Operations research, 90C Programació matemàtica, US voting rights, Programming (Mathematics), social and behavioral sciences, bancs de dades, Classificació AMS::91 Game theory, Lphom, economics, Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming, Classificació AMS::05 Combinatorics, Ecological inference, automatització, 91A Teoria de jocs, Voter transitions, dades transmissió
Document Type:
Academic journal
Article
File Description:
application/pdf; application/zip
Language:
English
DOI:
10.57645/20.8080.02.4
Access URL:
Rights:
CC BY NC ND
Accession Number:
edsair.dedup.wf.002..21856b9d4c6218b014e6e7af97bf53af
Database:
OpenAIRE
Further Information
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.