Multinomial-Logit Modeling of Mexican-Americans’ Choice Among International Home-Improvement-Center Retailers

2015 ◽  
pp. 306-306
Author(s):  
Mark Peterson ◽  
John Wurst
Keyword(s):  
Mexican Americans ◽  
Multinomial Logit ◽  
Home Improvement ◽  
Logit Modeling

Logistic and multinomial-logit models: A brief review on their modifications and extensions

2021 ◽  
Vol 16 (4) ◽  
pp. 279-282
Author(s):  
Stan Lipovetsky
Keyword(s):  
Discrete Choice ◽  
Principal Component ◽  
Multinomial Logit ◽  
Singular Value ◽  
Logit Models ◽  
Applied Psychology ◽  
Multinomial Logit Models ◽  
Best Worst Scaling ◽  
Value Decomposition ◽  
Logit Modeling

The work presents various techniques of the logistic and multinomial-logit modeling with their modifications. These methods are useful for regression modeling with a binary or categorical outcome, structuring in regression and clustering, singular value decomposition and principal component analysis with positive loadings, and numerous other applications. Particularly, these models are employed in the discrete choice modeling and the best-worst scaling known in applied psychology and socio-economics studies.

scholarly journals Multinomial Logit Model Building via TreeNet and Association Rules Analysis: An Application via a Thyroid Dataset

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 287
Author(s):  
Pannapa Changpetch
Keyword(s):  
Association Rules ◽  
Logit Model ◽  
Model Building ◽  
Real Life ◽  
Predictive Ability ◽  
Multinomial Logit Model ◽  
Multinomial Logit ◽  
New Variables ◽  
Association Rules Analysis ◽  
Logit Modeling

A model-building framework is proposed that combines two data mining techniques, TreeNet and association rules analysis (ASA) with multinomial logit model building. TreeNet provides plots that play a key role in transforming quantitative variables into better forms for the model fit, whereas ASA is important in finding interactions (low- and high-order) among variables. With the implementation of TreeNet and ASA, new variables and interactions are generated, which serve as candidate predictors in building an optimal multinomial logit model. A real-life example in the context of health care is used to illustrate the major role of these newly generated variables and interactions in advancing multinomial logit modeling to a new level of performance. This method has an explanatory and predictive ability that cannot be achieved using existing methods.

Review of Mexican Americans and Alcohol.

1988 ◽  
Vol 33 (10) ◽  
pp. 922-922
Author(s):  
No authorship indicated
Keyword(s):  
Mexican Americans
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