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Multinomial logit
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1	Some Issues in Using PROC LOGISTIC for Binary Logistic Regression by David C. Schlotzhauer Contents Add to Reading List Source URL: www.ats.ucla.edu Language: English - Date: 2016-08-17 18:18:26 Statistics Regression analysis Logistic regression Logit Odds ratio Probit model Statistical classification Dependent and independent variables Multinomial logistic regression Dummy variable
2	On the Relevance of Irrelevant Alternatives ∗ Austin R. Benson Stanford University Add to Reading List Source URL: stanford.edu Language: English - Date: 2016-02-02 15:50:14 Regression analysis Statistical models Single-equation methods Choice modelling Discrete choice Multinomial logistic regression Independence of irrelevant alternatives Logit Logistic regression Mode choice NLOGIT
3	Discrete mixtures of GEV models Stephane Hess, Imperial College London & RAND Europe Michel Bierlaire, EPFL John W. Polak, Imperial College London Conference paper STRC 2005 Add to Reading List Source URL: www.idep.eco.usi.ch Language: English - Date: 2012-01-04 14:50:59 Statistical models Regression analysis Choice modelling Discrete choice Mode choice Mixed logit Probability distribution Multinomial logistic regression Logistic regression Likelihood function Mixture model Gibbs sampling
4	An Analysis of the Determinants of Household Energy Expenditures: Empirical Evidence from the Irish Household Budget Survey by John Eakins Submitted for the Degree of Doctor of Philosophy in Economics Add to Reading List Source URL: www.seec.surrey.ac.uk Language: English - Date: 2014-12-23 10:22:15 Regression analysis Statistics Categorical data Energy Choice modelling Discrete choice Efficient energy use Logit Limited dependent variable Water heating Ordered logit Multinomial logistic regression
5	An application of the Constrained Multinomial Logit (CMNL) for modelling dominated choice alternatives Francisco Martínez, University of Chile Ennio Cascetta, Francesca Pagliara, University of Naples Michel Bierlaire, T Add to Reading List Source URL: www.strc.ch Language: English - Date: 2008-11-24 07:42:20 Regression analysis Statistical models Decision theory Choice modelling Transportation planning Logit Multinomial logistic regression Logistic regression Discrete choice Utility Multiple-criteria decision analysis Trip distribution
6	Microsoft Word - Scagnolari_Maggi_2010.docx Add to Reading List Source URL: www.strc.ch Language: English - Date: 2010-08-28 08:10:44 Choice modelling Statistical models Regression analysis Categorical data Microeconomics Discrete choice Mixed logit Multinomial logistic regression Logit Logistic regression Rational choice theory Preference
7	Object-fine choice models for long-term decisions: which level of granularity is necessary? A review of literature VAN EGGERMOND Michael A.B.* , ERATH Alexander* , and AXHAUSEN Kay W.** * Add to Reading List Source URL: www.strc.ch Language: English - Date: 2012-05-16 11:55:24 Transportation planning Choice modelling Simulation Statistics Discrete choice Economy Trip distribution Mode choice UrbanSim Mixed logit Accessibility Multinomial logistic regression
8	Discrete mixtures of GEV models Stephane Hess, Imperial College London & RAND Europe Michel Bierlaire, EPFL John W. Polak, Imperial College London Conference paper STRC 2005 Add to Reading List Source URL: www.strc.ch Language: English - Date: 2008-12-12 11:45:48 Statistical models Regression analysis Choice modelling Discrete choice Mode choice Mixed logit Probability distribution Multinomial logistic regression Logistic regression Likelihood function Mixture model Gibbs sampling
9	Start: Haugesund, Rogaland, Norway End: Trondheim, Sør-Trøndelag, Norway Total Distance: Miles Estimated Total Time: 47 hours, 31 minutes Source: http://www.mappoint.msn.com (Jan 31, 2005) Add to Reading List Source URL: www.strc.ch Language: English - Date: 2008-12-12 11:41:35 Statistical models Choice modelling Single-equation methods Regression analysis Discrete choice Mixed logit Multinomial logistic regression Shortest path problem Logit Probit Logistic regression Pairwise comparison
10	A comparative analysis of implicit and explicit methods to model choice set generation Michel Bierlaire Ricardo Hurtubia Gunnar Flötteröd Add to Reading List Source URL: www.strc.ch Language: English - Date: 2009-09-16 13:42:09 Statistics Regression analysis Statistical models Choice modelling Discrete choice Multinomial logistic regression Logistic regression Expected utility hypothesis Logit Decision-making Mixed logit Mode choice

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