Predicting symbolic interval-valued data through symmetrical nonlinear regression
by Dailys Maite Aliaga Reyes; Renata Maria Cardoso Rodrigues De Souza; Francisco José De Azevêdo Cysneiros
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 12, No. 2, 2017

Abstract: We proposed a symmetrical nonlinear regression model to fit interval-valued data. An important feature of this new model is that the estimate and prediction are less sensitive in the presence of outliers than a nonlinear model proposed in the literature. Monte Carlo simulation studies have been developed to investigate the performance of the model on different scenarios in precense of some percentage of outliers. The results based on the mean magnitude of the relative errors are presented and discussed. The model was fitted to one real symbolic dataset with noticeable interval outliers, and the forecast accuracy has been considered.

Online publication date: Tue, 23-May-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com