A new approach to adaptive spline threshold autoregression by using Tikhonov regularization and continuous optimization. Issue 6 (18th August 2019)

• Record Type: Journal Article
• Authors: Yalaz, S.; Taylan, P.; Özkurt, F. Yerlikaya
• Is Part Of: Journal of statistics & management systems. Volume 22:Issue 6(2019)
• Page Start: 1127
• Page End: 1142
• Publication Date: 2019-08-18
• Subjects: 62M10 -- 62J07 -- 90C20; Time series -- Multivariate adaptive regression splines (MARS) -- Adaptive splines threshold autoregression (ASTAR) -- Tikhonov regularization -- Multiobjective optimization -- Conic quadratic programming (CQP); Statistics -- Periodicals; Mathematical models -- Periodicals; Mathematical models; Statistics; Periodicals; 519.5
• DOI: 10.1080/09720510.2019.1606320
• Languages: English
• ISSNs: 0972-0510

Abstract

Abstract: In this study adaptive spline threshold autoregression and conic quadratic programming is used to develope conic adaptive spline threshold autoregression. With the introduced approach the second stepwise algorithm of adaptive spline threshold autoregression model turned to the Tikhonov regularization problem which was transformed into conic quadratic programming problem. The aim is to attain an optimum solution chosen in many solutions obtained by determining the bounds of the optimization problem using multiobjective optimization approach. Furthermore, in application part we used two different data set to compare performances of linear regression, adaptive spline threshold autoregression and conic adaptive spline threshold autoregression approaches.