A double sampling scheme for esimating from dependent binary data

Ünal, Cemal


A sequential classification algorithm for autoregressive processes
Otlu, Güneş; Candan, Çağatay; Çiloğlu, Tolga; Department of Electrical and Electronics Engineering (2011)
This study aims to present a sequential method for the classification of the autoregressive processes. Different from the conventional detectors having fixed sample size, the method uses Wald’s sequential probability ratio test and has a variable sample size. It is shown that the suggested method produces the classification decisions much earlier than fixed sample size alternative on the average. The proposed method is extended to the case when processes have unknown variance. The effects of the unknown pro...
A nested iterative scheme for computation of incompressible flows in long domains
Manguoğlu, Murat; Tezduyar, Tayfun E.; Sathe, Sunil (Springer Science and Business Media LLC, 2008-12-01)
We present an effective preconditioning technique for solving the nonsymmetric linear systems encountered in computation of incompressible flows in long domains. The application category we focus on is arterial fluid mechanics. These linear systems are solved using a nested iterative scheme with an outer Richardson scheme and an inner iteration that is handled via a Krylov subspace method. Test computations that demonstrate the robustness of our nested scheme are presented.
A memetic algorithm for clustering with cluster based feature selection
Şener, İlyas Alper; İyigün, Cem; Department of Operational Research (2022-8)
Clustering is a well known unsupervised learning method which aims to group the similar data points and separate the dissimilar ones. Data sets that are subject to clustering are mostly high dimensional and these dimensions include relevant and redundant features. Therefore, selection of related features is a significant problem to obtain successful clusters. In this study, it is considered that relevant features for each cluster can be varied as each cluster in a data set is grouped by different set of fe...
A Path-Finding Based Method for Concept Discovery in Graphs
Abay, Nazmiye Ceren; Mutlu, Alev; Karagöz, Pınar (2015-07-08)
In the multi-relational data mining, concept discovery is the problem of inducing definitions of a relation in terms of other relations provided. In this paper, we present a method that combines graph-based and association rule mining-based methods for concept discovery in graphs. The proposed method is related to graphs as the data, which is initially stored in a relational database, is represented as a graph and concept descriptors are the paths that connect certain vertices; and it is related to associat...
A fuzzy linguistic decision model approach for selecting the optimum promotion mix for digital products with genetic algorithms
Gün, Mustafa Murat; İşler, Veysi; Department of Computer Engineering (2010)
Promotion is one of the four major marketing elements of the marketing mix (others are product, price and place) in implementing marketing strategy. Promotion is dealing with the ways a company communicates with its customers to persuade them to buy the product. Promotion mix covers all the different ways a company choose to communicate with its customers such as advertising, personnel selling, PR, sales promotion and others. Selecting the optimal blend of the promotion mix is a tough and critical issue for...
Citation Formats
C. Ünal, “A double sampling scheme for esimating from dependent binary data,” Middle East Technical University, 1990.