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

fYear

2009

fDate

14-19 June 2009

Firstpage

2226

Lastpage

2229

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2009. IJCNN 2009. International Joint Conference on

Conference_Location

Atlanta, GA

ISSN

1098-7576

Print_ISBN

978-1-4244-3548-7

Electronic_ISBN

1098-7576

Type

conf

DOI

10.1109/IJCNN.2009.5178711

Filename

5178711