Altay, Umut
Akkaya, Ayşen
Yucemen, M Semih


Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market
Özmen, Ayşe; Zinchenko, Yuriy; Weber, Gerhard Wilhelm (2022-01-01)
Currently, the presence of data uncertainty and noise raises critical issues to be handled on both theoretical and computational grounds. Therefore, robustification and robust optimization have gained attention from theoretical and practical points of view for the establishment of a modeling framework in mathematical optimization to immunize solutions against diverse uncertainties. Data of both the input and output variables, underlying the problems to be addressed, are affected by the noise of different ki...
Robust estimation in multivariate heteroscedastic regression models with autoregressive covariance structures using EM algorithm
GÜNEY, YEŞİM; ARSLAN, OLÇAY; Gökalp Yavuz, Fulya (2022-09-01)
© 2022 Elsevier Inc.In the analysis of repeated or clustered measurements, it is crucial to determine the dynamics that affect the mean, variance, and correlations of the data, which will be possible using appropriate models. One of these models is the joint mean–covariance model, which is a multivariate heteroscedastic regression model with autoregressive covariance structures. In these models, parameter estimation is usually carried on under normality assumption, but the resulting estimators will be very ...
Robust estimation and hypothesis testing under short-tailedness and inliers
Akkaya, Ayşen (Springer Science and Business Media LLC, 2005-06-01)
Estimation and hypothesis testing based on normal samples censored in the middle are developed and shown to be remarkably efficient and robust to symmetric short-tailed distributions and to inliers in a sample. This negates the perception that sample mean and variance are the best robust estimators in such situations (Tiku, 1980; Dunnett, 1982).
Robust estimation and hypothesis testing in microarray analysis
Ülgen, Burçin Emre; Akkaya, Ayşen; Department of Statistics (2010)
Microarray technology allows the simultaneous measurement of thousands of gene expressions simultaneously. As a result of this, many statistical methods emerged for identifying differentially expressed genes. Kerr et al. (2001) proposed analysis of variance (ANOVA) procedure for the analysis of gene expression data. Their estimators are based on the assumption of normality, however the parameter estimates and residuals from this analysis are notably heavier-tailed than normal as they commented. Since non-no...
Robust parameter design of products and processes with an ordinal categorical response using random forests
Gülbudak Dil, Seçil; Köksal, Gülser; Department of Industrial Engineering (2018)
In industrial organizations, manufacturers aim to achieve target product performance with minimum variation. For that reason, finding optimal settings of product and process design parameters that make it possible to consistently achieve target product performance is an important design problem. In this study, we propose an alternative method to solve this problem for the case of an ordinal categorical product/process response. The method utilizes Random Forest (RF) for modelling mean and variance of the re...
Citation Formats
U. Altay, A. Akkaya, and M. S. Yucemen, “ROBUST AND BAYESIAN PARAMETER ESTIMATION IN TIME DEPENDENT SEISMIC HAZARD ANALYSIS,” 2017, Accessed: 00, 2021. [Online]. Available: