Logit

The logit ( /ˈloʊdʒɪt/ LOH-jit) function is the inverse of the sigmoidal "logistic" function used in mathematics, especially in statistics.

Log-odds and logit are synonyms.

Read more about LogitDefinition, History, Uses and Properties, Comparison With Probit

Other articles related to "logit":

Mixed Logit - Unrestricted Substitution Patterns
... The mixed logit model can represent general substitution pattern because it does not exhibit logit's restrictive independence of irrelevant alternatives (IIA) property ... one alternative need not imply (as with logit) a ten-percent reduction in each other alternative." The relative percentages depend on correlation between the ...
Mixed Logit - Random Taste Variation
... The standard logit model's "taste" cofficients, or 's, are fixed, which means the 's are the same for everyone ... Mixed logit has different 's for each person (i.e ... each decision maker.) In the standard logit model, the utility of person n for alternative i is with ~ iid extreme value For the mixed logit model, this specification ...
Local Independence Of Irrelevant Alternatives - In Econometrics
... IIA is a property of the multinomial logit and the conditional logit models in econometrics outcomes that could theoretically violate this IIA (such as the outcome of multicandidate elections or any choice made ... value, multinomial probit (also called conditional probit) and mixed logit are alternative models for nominal outcomes which relax IIA, but these models often ... The most popular of these is called the nested logit model ...
Logit - Comparison With Probit
... Closely related to the logit function (and logit model) are the probit function and probit model ... The logit and probit are both sigmoid functions with a domain between 0 and 1, which makes them both quantile functions — i.e ... In fact, the logit is the quantile function of the logistic distribution, while the probit is the quantile function of the normal distribution ...
Common Features of Discrete Choice Models
... models take many forms, including Binary Logit, Binary Probit, Multinomial Logit, Conditional Logit, Multinomial Probit, Nested Logit, Generalized Extreme Value Models, Mixed Logit, and Exploded Logit ...