Logistic regression model assumptions

Nelder and Wedderburn (1972) extended the general linear model in two ways. First, they relaxed the assumption that the observations have the normal distribution to allow the observations to come from some one-dimensional exponential family, not necessarily normal. Second, instead of requiring the mean of the observations to equal a linear function of the predictor, they allowed a function of the mean to be linked to (set equal to) the linear predictor. They named this the generalized linear model and called the function set equal to the linear predictor the link function. The logistic regression model satisfies the assumptions of the generalized linear model. They are ... 

Another classification technique is logistic regression [76], which is based on the assumption that a sigmoidal dependency exists between the probability of group membership and one or more predictor variables. It has been used [72] to model eye irritation data. 

LDA assumes normal distribution of the explanatory variables, while logistic regression does not. If the Gaussian assumptions are met, then LDA is a more powerful and efficient model than logistic regression. 

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