Possibilistic logistic regression for fuzzy categorical response data

Author

Namdari, Mahshid ; Taheri, Sayed Mostafa ; Abadi, Aharon ; Rezaei, Mahdi ; Kalantari, Nader

Author_Institution

Dept. of Biostat., Shahid Beheshti Univ. of Med. Sci., Tehran, Iran

fYear

2013

fDate

7-10 July 2013

Firstpage

1

Lastpage

6

Abstract

A new possibilistic logistic regression is investigated, which can be used in cases where the explanatory variables are crisp observations but the values of the response variable are non-precise and are measured by linguistic terms. For evaluating the model, a goodness-of-fit criterion which is called the mean of capability index is employed. A numerical example in a real clinical study about child´s appetite status is given to explain the method.

Keywords

category theory; computational linguistics; fuzzy set theory; possibility theory; regression analysis; capability index; child appetite status; crisp observations; explanatory variables; fuzzy categorical response data; goodness-of-fit criterion; linguistic terms; possibilistic logistic regression; Computational modeling; Data models; Diseases; Educational institutions; Logistics; Medical diagnostic imaging; Pragmatics; Fuzzy logistic regression; appetite; linguistic variable; possiblistic odds;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems (FUZZ), 2013 IEEE International Conference on

Conference_Location

Hyderabad

ISSN

1098-7584

Print_ISBN

978-1-4799-0020-6

Type

conf

DOI

10.1109/FUZZ-IEEE.2013.6622457

Filename

6622457