Strong laws for weighted sums of ψ-mixing random variables and applications in errors-in-variables regression models

• Di Hu ^[1] ; Pingyan Chen ^[1] ; Soo Hak Sung ^[2]
  1. [1] Jinan University

     Jinan University

     China

     
  2. [2] Pai Chai University

     Pai Chai University

     Corea del Sur

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 26, Nº. 3, 2017, págs. 600-617
• Idioma: inglés
• DOI: 10.1007/s11749-017-0526-6
• Texto completo no disponible (Saber más ...)
• Resumen

  • In this paper, we establish strong laws for weighted sums of identically distributed ψ-mixing random variables without any conditions on mixing rate. The classical Kolmogorov strong law of large numbers is extended to weighted sums of ψ-mixing random variables. Two types of weights are considered for the weighted sums. These results are applied to the least-squares estimators in the simple linear errors-in-variables regression model when the errors are ψ-mixing random vectors.