Robust Linear Mixed Models with Kotz type Distributions

9th International Statistics Day Symposium (10 - 14 Mayıs 2014)


Robust semi supervised clustering with polyhedral and circular uncertainty
Dinler, Derya; Tural, Mustafa Kemal (null; 2016-07-03)
We consider a semi-supervised clustering problem where the locations of the data objects are subject to uncertainty. Each uncertainty set is assumed to be either a closed convex bounded polyhedron or a closed disk. The final clustering is expected to be in accordance with a given number of instance level constraints. The objective function considered minimizes the total of the sum of the violation costs of the unsatisfied instance level constraints and a weighted sum of squared maximum Euclidean distances b...
Robust hyper-heuristic algorithms for the offline oriented/non-oriented 2D bin packing problems
Beyaz, Muhammed; Dokeroglu, Tansel; Coşar, Ahmet (2015-11-01)
The offline 2D bin packing problem (2DBPP) is an NP-hard combinatorial optimization problem in which objects with various width and length sizes are packed into minimized number of 2D bins. Various versions of this well-known industrial engineering problem can be faced frequently. Several heuristics have been proposed for the solution of 2DBPP but it has not been possible to find the exact solutions for large problem instances. Next fit, first fit, best fit, unified tabu search, genetic and memetic algorith...
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...
Robust estimation in multiple linear regression model with non-Gaussian noise
Akkaya, Ayşen (2008-02-01)
The traditional least squares estimators used in multiple linear regression model are very sensitive to design anomalies. To rectify the situation we propose a reparametrization of the model. We derive modified maximum likelihood estimators and show that they are robust and considerably more efficient than the least squares estimators besides being insensitive to moderate design anomalies.
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 ...
Citation Formats
F. Gökalp Yavuz, “Robust Linear Mixed Models with Kotz type Distributions,” presented at the 9th International Statistics Day Symposium (10 - 14 Mayıs 2014), 2014, Accessed: 00, 2021. [Online]. Available: