An alternative algorithm of the empirical likelihood estimation for the parameter of a linear regression model

ÖZDEMİR Ş., ARSLAN O.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.48, sa.7, ss.1913-1921, 2019 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 48 Sayı: 7
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1080/03610918.2018.1435801
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1913-1921
  • Anahtar Kelimeler: Empirical likelihood, lagrange multipliers, linear regression, CONFIDENCE-INTERVALS
  • Ankara Üniversitesi Adresli: Evet

Özet

Empirical likelihood (EL) is a nonparametric method based on observations. EL method is defined as a constrained optimization problem. The solution of this constrained optimization problem is carried on using duality approach. In this study, we propose an alternative algorithm to solve this constrained optimization problem. The new algorithm is based on a newton-type algorithm for Lagrange multipliers for the constrained optimization problem. We provide a simulation study and a real data example to compare the performance of the proposed algorithm with the classical algorithm. Simulation and the real data results show that the performance of the proposed algorithm is comparable with the performance of the existing algorithm in terms of efficiencies and cpu-times.