Proposal of some metaheuristic optimization oriented biasing parameter selection methods in Almon distributed lag model: link between air pollution and mortality

ÖZBAY N.

Stochastic Environmental Research and Risk Assessment, 2025 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1007/s00477-025-03073-2
  • Dergi Adı: Stochastic Environmental Research and Risk Assessment
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, Environment Index, Geobase, Index Islamicus, zbMATH
  • Anahtar Kelimeler: Almon distributed lag model, Biased estimation, Metaheuristic optimization, Monte Carlo simulation, Mortality
  • Çukurova Üniversitesi Adresli: Evet

Özet

Genetic algorithm, simulated annealing, and particle swarm optimization are well-regarded metaheuristics due to performing remarkably for solving diverse optimization problems. In this article, some genetic algorithm, simulated annealing, and particle swarm optimization oriented biasing parameter selection methods are proposed for a new two-parameter biased estimator defined for the Almon distributed lag model suffering from multicollinearity. Additional novel hybrid biasing parameter selection methods in which the genetic algorithm, simulated annealing, and particle swarm optimization are consolidated with conventional parameter selection are also introduced to determine biasing parameters of the proposed estimator. To display how the new metaheuristic optimization oriented biasing parameter selection methods perform in practice, relationship between air pollution and mortality in the United States is modeled by the Almon distributed lag model. More comprehensive results are accomplished with a Monte Carlo simulation experiment designed for the Almon distributed lag model and proposed metaheuristics. Empirical analyses provide outcomes promoting the proposed metaheuristic optimization oriented biasing parameter selection methods.