Evidence Optimization for Consequently Generated Models

Strijov, Vadim
Krymova, Katya
Weber, Gerhard Wilhelm
To construct as adequate regression model one has to fulfill the set of measured features with their generated derivatives. Often the number of these features exceeds the number of the samples in the data set. After a feature generation process the problem of feature selection from a set of highly correlated features arises. The proposed algorithm uses an evidence maximization procedure to select a model as a subset of generated features. During the selection process it rejects multicollinear features. A problem of European option volatility modelling illustrates the algorithm. Its performance is compared with performance of similar well-known algorithms.


Multiple linear regression model with stochastic design variables
İslam, Muhammed Qamarul (Informa UK Limited, 2010-01-01)
In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.
Hypothesis testing in one-way classification AR(1) model with Student’s t innovations: An application to a real life data
Yıldırım, Özgecan; Yozgatlıgil, Ceylan; Şenoğlu, Birdal (null; 2017-05-26)
In this study, we estimate the model parameters in one-way classification AR (1) model when the distribution of the error terms is independently and identically distributed (iid) Student’s t. Maximum likelihood (ML) methodology is used in the estimation procedure. We also introduce the F statistic based on the ML estimators of the parameters for testing the equality of the treatment means. See also Yıldırım (2017) (M.S. Thesis, METU, Ankara, Continue) and Şenoğlu and Bayrak (2016) (Linear Contrasts in one-w...
Regression analysis with a dtochastic design variable
Sazak, HS; Tiku, ML; İslam, Muhammed Qamarul (Wiley, 2006-04-01)
In regression models, the design variable has primarily been treated as a nonstochastic variable. In numerous situations, however, the design variable is stochastic. The estimation and hypothesis testing problems in such situations are considered. Real life examples are given.
Classification models based on Tanaka's fuzzy linear regression approach: The case of customer satisfaction modeling
Fuzzy linear regression (FLR) approaches are widely used for modeling relations between variables that involve human judgments, qualitative and imprecise data. Tanaka's FLR analysis is the first one developed and widely used for this purpose. However, this method is not appropriate for classification problems, because it can only handle continuous type dependent variables rather than categorical. In this study, we propose three alternative approaches for building classification models, for a customer satisf...
Analytical modeling of a time-threshold based multi-guard bandwidth allocation scheme for cellular networks
Candan, Idil; Salamah, Muhammed (2009-05-28)
In this paper, the analytical modeling of a time-threshold based multi-guard bandwidth allocation scheme is presented using a two-dimensional markov chain. The main idea of the scheme is based on monitoring the elapsed real time of handoff calls and according to a time threshold (t(e)), a handoff call is either slightly-prioritized or fully-prioritized. A slightly-prioritized handoff call has higher priority than a new call and lower priority than a fully-prioritized handoff call. Also in this paper, the nu...
Citation Formats
V. Strijov, K. Krymova, and G. W. Weber, “Evidence Optimization for Consequently Generated Models,” 2010, vol. 1337, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54143.