Data wrangling, computational burden, automation, robustness and accuracy in ecological inference forecasting of RxC tables

  1. Jose M. Pavía 1
  2. Rafael Romero 2
  1. 1 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

  2. 2 Universidad Politécnica de Valencia
    info

    Universidad Politécnica de Valencia

    Valencia, España

    ROR https://ror.org/01460j859

Revista:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Ano de publicación: 2023

Volume: 47

Número: 1

Páxinas: 151-186

Tipo: Artigo

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Outras publicacións en: Sort: Statistics and Operations Research Transactions

Resumo

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.

Fonte de datos: Dialnet