Skip to Main Content (Press Enter)

Logo UNIBS
  • ×
  • Home
  • People
  • Organizations
  • Expertise & Skills
  • Outputs
  • Jobs
  • Degrees
  • Courses
  • Third Mission

Expertise & Skills
Logo UNIBS

|

Expertise & Skills

unibs.it
  • ×
  • Home
  • People
  • Organizations
  • Expertise & Skills
  • Outputs
  • Jobs
  • Degrees
  • Courses
  • Third Mission
  1. Outputs

A Recent Approach to Derive the Multinomial Logit Model for Choice Probability

Chapter
Publication Date:
2018
Abstract:
It is well known that the Multinomial Logit model for the choice probability can be obtained by considering a random utility model where the choice variables are independent and identically distributed with a Gumbel distribution. In this paper we organize and summarize existing results of the literature which show that using some results of the extreme values theory for i.i.d. random variables, the Gumbel distribution for the choice variables is not necessary anymore and any distribution which is asymptotically exponential in its tail is sufficient to obtain the Multinomial Logit model for the choice probability.
CRIS type:
2.1 Contributo in volume (Capitolo o Saggio)
Keywords:
Random utility; Extreme values theory; Asymptotic approximation; Multinomial Logit model
List of contributors:
Tadei, Roberto; Perboli, Guido; Manerba, Daniele
Authors of the University:
MANERBA Daniele
Models and Algorithms for Optimization
Handle:
https://iris.unibs.it/handle/11379/526345
Book title:
New Trends in Emerging Complex Real Life Problems
Published in:
AIRO SPRINGER SERIES
Series
  • Support
  • Privacy
  • Use of cookies
  • Legal notes

Powered by VIVO | Designed by Cineca | 26.5.2.0