Inference of time series chain graphical model

Farnoudkia, Hajar
Purutçuoğlu Gazi, Vilda


Inference in multivariate linear regression models with elliptically distributed errors
İslam, Muhammed Qamarul; Yazici, Mehmet (2014-08-01)
In this study we investigate the problem of estimation and testing of hypotheses in multivariate linear regression models when the errors involved are assumed to be non-normally distributed. We consider the class of heavy-tailed distributions for this purpose. Although our method is applicable for any distribution in this class, we take the multivariate t-distribution for illustration. This distribution has applications in many fields of applied research such as Economics, Business, and Finance. For estimat...
Inference of large-scale networks via statistical approaches
Ayyıldız Demirci, Ezgi; Purutçuoğlu Gazi, Vilda; Department of Statistics (2019)
In system biology, the interactions between components such as genes, proteins, can be represented by a network. To understand the molecular mechanism of complex biological systems, construction of their networks plays a crucial role. However, estimation of these networks is a challenging problem because of their high dimensional and sparse structures. The Gaussian graphical model (GGM) is widely used approach to construct the undirected networks. GGM define the interactions between species by using the con...
Inference for variance components in a mixed model for unbalanced split plot design
Guven, B (Informa UK Limited, 2005-01-01)
We consider the unbalanced split-plot design with the whole plot and the subplot effect from nonnormal universes. The three estimators for the whole plot effect variance component are obtained. An approximate test for significance of the whole plot effect variance component is presented.
Inference of Biological Networks via Random Forest Algorithm
Purutçuoğlu Gazi, Vilda (2015-06-01)
Inference of switching networks by using a piecewise linear formulation
Akçay, Didem; Öktem, Hakan; Department of Scientific Computing (2005)
Inference of regulatory networks has received attention of researchers from many fields. The challenge offered by this problem is its being a typical modeling problem under insufficient information about the process. Hence, we need to derive the apriori unavailable information from the empirical observations. Modeling by inference consists of selecting or defining the most appropriate model structure and inferring the parameters. An appropriate model structure should have the following properties. The model...
Citation Formats
H. Farnoudkia and V. Purutçuoğlu Gazi, “Inference of time series chain graphical model,” 2018, Accessed: 00, 2021. [Online]. Available: