EconPapers    
Economics at your fingertips  
 

Regression models for Boolean random sets

M. Khazaee and K. Shafie

Journal of Applied Statistics, 2006, vol. 33, issue 5, 557-567

Abstract: In this paper we consider the regression problem for random sets of the Boolean-model type. Regression modeling of the Boolean random sets using some explanatory variables are classified according to the type of these variables as propagation, growth or propagation-growth models. The maximum likelihood estimation of the parameters for the propagation model is explained in detail for some specific link functions using three methods. These three methods of estimation are also compared in a simulation study.

Keywords: Random closed set; Boolean model; generalized linear model (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760600585683 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:33:y:2006:i:5:p:557-567

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760600585683

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:japsta:v:33:y:2006:i:5:p:557-567