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Experimental evolution of Plasmodium yoelii in single and helminth-coinfected mice. [PDF]
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The Stata Journal: Promoting communications on statistics and Stata, 2022
The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive and computational mistakes. In this article, I review a menu of options to interpret the results of logistic regressions correctly and effectively using Stata.
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The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive and computational mistakes. In this article, I review a menu of options to interpret the results of logistic regressions correctly and effectively using Stata.
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Journal of Marketing Research, 1986
The typical testing for equality of parameters across several response functions cannot be performed when the dependent variable is a probability. The authors investigate the pooling issues of the response function when the model is specified as a transformational logit.
Hubert Gatignon, David J. Reibstein
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The typical testing for equality of parameters across several response functions cannot be performed when the dependent variable is a probability. The authors investigate the pooling issues of the response function when the model is specified as a transformational logit.
Hubert Gatignon, David J. Reibstein
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2014
The ordered logit model is a regression model for an ordinal response variable. The model is based on the cumulative probabilities of the response variable: in particular, the logit of each cumulative probability is assumed to be a linear function of the covariates with regression coefficients constant across response categories.
Grilli, Leonardo, Rampichini, Carla
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The ordered logit model is a regression model for an ordinal response variable. The model is based on the cumulative probabilities of the response variable: in particular, the logit of each cumulative probability is assumed to be a linear function of the covariates with regression coefficients constant across response categories.
Grilli, Leonardo, Rampichini, Carla
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Transportation Research Part B: Methodological, 1991
The logit model is the simplest and best-known probabilistic choice model. Nevertheless according to the deficient flexibility there are problems of making use of the multinomial logit model. In this paper a generalized logit model, which is essentially more flexible than the traditional multinomial logit model, is presented.
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The logit model is the simplest and best-known probabilistic choice model. Nevertheless according to the deficient flexibility there are problems of making use of the multinomial logit model. In this paper a generalized logit model, which is essentially more flexible than the traditional multinomial logit model, is presented.
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Statistica Neerlandica, 1988
This paper reviews aspects of the application of logit models in economics. We consider some economic models that lead to a simple or a multinomial logit specification. A detailed account is given of the possible specifications of the multinomial model. We stress the relationship between distributional assumptions and functional form assumptions. Some (
Cramer, J. S., Ridder, G.
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This paper reviews aspects of the application of logit models in economics. We consider some economic models that lead to a simple or a multinomial logit specification. A detailed account is given of the possible specifications of the multinomial model. We stress the relationship between distributional assumptions and functional form assumptions. Some (
Cramer, J. S., Ridder, G.
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