A new composite lognormal-Pareto type II regression model to analyze household budget data via particle swarm optimization

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SOFT COMPUTING, vol.26, no.5, pp.2391-2408, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.1007/s00500-021-06641-0
  • Journal Name: SOFT COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.2391-2408
  • Keywords: Composite regression model, Particle swarm optimization, Complex survey design, Household budget survey, Lognormal regression model, Lomax regression model, MIXTURES
  • Hacettepe University Affiliated: Yes


When data exhibits heavy-tailed behavior, traditional regression approaches might be inadequate or inappropriate to model the data. In such data analyses, composite models, which are built by piecing together two or more weighted distributions at specified threshold(s), are alternative models. When data contain covariate information, composite regression models can be used. In the existing literature, there is not much work done on this topic. The only study is Gan and Valdez (N Am Actuar J 22(4):554-573, 2018) paper. In this study, a novel Lognormal-Pareto type II composite regression model is proposed. Particle swarm optimization (PSO) is performed to obtain model parameters of the proposed model. The proposed model is applied to model monthly consumption expenditure and affecting factors. The data is obtained from the National Household Budget Survey, which is conducted annually by the Turkish Statistical Institute (TurkStat). Since the sampling design of the Household Budget Survey is stratified two-stage cluster sampling, the parameters are estimated under weighted data by updating the proposed model and PSO. Additionally, the proposed regression model performance is compared with Lognormal, Lomax, Gamma, and Gamma-Pareto type II regression models. The results demonstrate that the proposed model provides an improved fit to data.