The first part of this paper defines recursive interactions by means of logistic functions and derives a general result on the way interacting systems evolve in attractors. It also defines the notion of coevolution trajectory and presents a new family of attractors: orbital attractors (including single, irregular, folded, complex and discontinuous orbits). The second part summarizes the results...

This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples. If the radius of this ball is chosen judiciously, we can guarantee that it contains the unknown datagenerating distribution with high confidence. We then formulat...

This chapter describes a tree-structured extension and generalization of the logistic regression method for fitting models to a binary-valued response variable. The technique overcomes a significant disadvantage of logistic regression, which is interpretability of the model in the face of multicollinearity and Simpson’s paradox. Section 1 summarizes the statistical theory underlying the logisti...

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...

In many industrial and non-industrial applications the quality of a process or product is characterized by a relationship between a response variable and one or more explanatory variables. This relationship is referred to as profile. In the past decade, profile monitoring has been extensively studied under the normal response variable, but it has paid a little attention to the profile with the ...

For the most part, this book concerns itself with measurement data and the corresponding analyses based on normal distributions. In this chapter and the next we consider data that consist of counts. Elementary count data were introduced in Chapter 5. Frequently data are collected on whether or not a certain event occurs. A mouse dies when exposed to a dose of chloracetic acid or it does not. In...

The aim of this paper is to describe an innovative Logistic Game® developed by the authors. It creates a competition between different teams in terms of strategic decisions in logistic and production problems in a real production system. The game is innovative in three ways. The first is the type of game. It is a game representing a production and logistic system, with the dynamic problems of a...

©FSRH J Fam Plann Reprod Health Care 2008: 34(3) What is it? When a response variable has only two possible values (e.g. recurrence/not), binary logistic regression is commonly used to test or model the association between that response and a number of potential explanatory variables, with each association estimated in terms of an odds ratio (OR). Multinomial logistic regression is an extension...

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the associati...