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Fatma Yerlikaya-Özkurt
Researcher at Atılım University
Publications - 14
Citations - 260
Fatma Yerlikaya-Özkurt is an academic researcher from Atılım University. The author has contributed to research in topics: Multivariate adaptive regression splines & Nonparametric regression. The author has an hindex of 7, co-authored 13 publications receiving 211 citations. Previous affiliations of Fatma Yerlikaya-Özkurt include Middle East Technical University.
Papers
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Journal ArticleDOI
CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization
TL;DR: In this article, the authors used a penalized residual sum of squares for MARS as a Tikhonov regularization problem, and treated this with continuous optimization technique, in particular, the framework of conic quadratic programming.
Journal ArticleDOI
An alternative approach to the ground motion prediction problem by a non-parametric adaptive regression method
TL;DR: In this article, a new prediction algorithm, called Conic Multivariate Adaptive Regression Splines (CMARS), is employed on an available dataset for deriving a new ground motion prediction equation.
Journal ArticleDOI
On the foundations of parameter estimation for generalized partial linear models with B-splines and continuous optimization
TL;DR: This paper model and treat the constrained P-IRLS problem by using the elegant framework of conic quadratic programming and approaches solving the P- IRLS problem using continuous optimization techniques.
Journal ArticleDOI
Estimation of the Hurst parameter for fractional Brownian motion using the CMARS method
TL;DR: In this article, an alternative method for estimating the Hurst parameter using the conic multivariate adaptive regression splines (CMARS) method was developed, which is superior to others in that it not only estimates the HURST parameter but also finds spline parameters of the stochastic process in an adaptive way.
Book ChapterDOI
A Review and New Contribution on Conic Multivariate Adaptive Regression Splines (CMARS): A Powerful Tool for Predictive Data Mining
TL;DR: It is indicated that currently the CMARS method is a powerful alternative to the MARS algorithm as well as the other predictive data mining tools, and thus, deserves more attention to evaluate and develop it even further.