This paper examines the ability of neural based structures to model the logistic equation. This modelling includes not only the generation of the logistic curve, but also the time series that are generated by the logistic neural model. This study concerns all main regions of the logistic equation: the region of convergence for parameter values less than 3, the periodic region for parameter valu...

Logistic regression models are a popular and effective method to predict the probability of categorical response data. However, inference for these can become computationally prohibitive large datasets. Here we adapt ideas from symbolic data analysis summarize collection predictor variables into histogram form, perform on this summary dataset. We develop based composite likelihoods derive an ef...