Bivariate generalized linear model for interval-valued variables

Authors:
Eufrásio de A. Lima Neto;Gauss M. Cordeiro;Francisco A. T. de Carvalho;Ulisses U. dos Anjos;Abner G. da Costa
Affiliations:
Departamento de Estatística, Universidade Federal da Paraíba, Joao Pessoa, PB, Brazil;Departamento de Estatística Informática, Universidade Federal Rural de Pernambuco, Recife, PE, Brazil;Centro de Informatica, Universidade Federal de Pernambuco, Recife, PE, Brasil;Departamento de Estatística, Universidade Federal da Paraíba, Joao Pessoa, PB, Brazil;Departamento de Estatística, Universidade Federal da Paraíba, Joao Pessoa, PB, Brazil
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 5
Cited 1

Applied multivariate statistical analysis

Applied multivariate statistical analysis
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data

Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)

Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
Testing linear independence in linear models with interval-valued data

Computational Statistics & Data Analysis
Centre and Range method for fitting a linear regression model to symbolic interval data

Computational Statistics & Data Analysis

A resampling approach for interval-valued data regression

Statistical Analysis and Data Mining

Quantified Score

Hi-index	0.00

Visualization

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.