Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/13832

Title: Statistical Models for categorical data: brief review for applications in Ecology
Authors: Ramos, Rosario
Oliveira, Manuela
Borges, José
McDill, Marc E
Keywords: categorical data
Presence/Absence models
Generalized linear models
Generalized additive models
Issue Date: 28-Sep-2014
Publisher: AIP Publishing
Citation: AIP Conference Proceedings 1648, 840015 (2015); doi: 10.1063/1.4913055 Ano de publicação - 2015
Abstract: A brief review of statistical models for prediction of categorical data is presented, with emphasis on the binary type. Several methods have been adopted to build predictive models for binary and other types of categorical data and response variables. The focus here is on generalized linear models and generalized additive models, widely applied in problems in Ecology, when the goal is to fit a model to data of presence/absence type or any other categorical response. The estimation methods used for generalized linear models and generalized additive models as well its statistical properties are discussed. Some examples in ecology are addressed
URI: http://dx.doi:10.101063/1.4913055
http://hdl.handle.net/10174/13832
Type: article
Appears in Collections:CIMA - Artigos em Livros de Actas/Proceedings

Files in This Item:

File Description SizeFormat
Ramos et al. 2014.pdf166.46 kBAdobe PDFView/OpenRestrict Access. You can Request a copy!
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois