An Adjusted Iterative Algorithmic Approach for the Maximum Likelihood Estimation of the Pseudo-Copula Regression: P-MBP

Erdemir Ö. G.

SOFT COMPUTING, ss.1-15, 2023 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1007/s00500-023-08722-8
  • Dergi Adı: SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.1-15
  • Hacettepe Üniversitesi Adresli: Evet

Özet

In this study, a pseudo-maximization by parts method is introduced by developing the maximization by parts algorithm for

the parameter estimation of pseudo-copula regression models. Sub- and main score equations are obtained from the

pairwise log-likelihood function and solved by the proposed iterative algorithm. The pseudo-maximization by parts

algorithm is an iterative algorithm to avoid having to calculate the second-order derivative of the full log-likelihood

function as maximization by parts algorithm. Instead of the Gaussian copula function in maximization by parts algorithm,

the pseudo-Gaussian copula function is included in the new algorithm. The mean square errors of the estimators found by

the maximization by parts algorithm and the pseudo-maximization by parts algorithm are compared using real Turkish

comprehensive insurance data taken from the Turkish Insurance Information and Monitoring Center for the year 2017, and

it is notable that the proposed algorithm provided better results in terms of having lower errors.