Title :
Bivariate Generalized Linear Model for Interval-Valued Variables
Author :
de A.Lima Neto, E. ; Cordeiro, Gauss M. ; De Carvalho, Francisco A T ; Anjos, Ulisses U dos ; Costa, Abner G da
Author_Institution :
Dept. de Estatastica, Univ. Fed. da Paraiba, Joao Pessoa, Brazil
Abstract :
Current symbolic regression methods visualize problems from an optimization point of view and do not consider the probabilistic aspects related to regression models. In this paper, we present the bivariate generalized linear model (BGLM) proposed by Iwasaki and Tsubaki [5] in the context of interval-valued data sets. Important aspects related to the BGLM that remain open or can be improved will be considered. The performance of this new approach in relation to symbolic regression methods proposed by Billard and Diday [1] and Lima Neto and De Carvalho [7] will be considered through real interval data sets.
Keywords :
data analysis; regression analysis; bivariate generalized linear model; interval-valued data sets; interval-valued variables; symbolic data analysis; symbolic regression methods visualize problems; Data analysis; Data visualization; Gaussian processes; Linear regression; Meteorology; Neural networks; Optimization methods; Parameter estimation; Predictive models; Upper bound;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5178711
