Generalized logistic model

Because of the large number of aspects that must be covered by the model, it is important to have it well structured for further maintenance of the decision support system. A simplified representation is shown in Figure 5.8. The modeling was done such that the sections defining the general logistics were clearly separated from those closely related to the process. Also, the parts that refer to input/output to the user interfaces were strictly kept apart from the model itself to allow a smoother development. 

Model building for the generalized additive logistic model proceeds in the same manner as described above for GAM with a continuous response variable. 

A software tool Trend Analysis for analysis the time series was applied. Trend analysis fits a general trend model to time series data and provides forecasts. S-curve is best fitted to our drying case. The S-curve model fits the Pearl-Reed logistic trend model. This accounts for the case where the series follows an S-shaped curve. The model is ... 

In other words, what is a main effect in logistic regression terms becomes an interaction in log-linear model terms. However, it is really the log-linear model that is the odd one out among generalized linear models as regards use of interactions and, in more conventional terms, Simpson s paradox does not involve interactions. 

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 ... 

The logistic regression model is an example of a generalized linear model. The observations come from a member of one-dimensional exponential family, in this case binomial. Each observation has its own parameter value that is linked to the linear predictor by a link function, in this case the logit link. The observations are all independent. 

In the first part of the model, s is calculated. This step is necessary to ensure comparability, due to a different understanding of logistics customer service in theory and practice. Generally, logistics customer service is operationalized into four service factors lead time, delivery reliability, quality and flexibility (Lambert Stock, 1993). Since the service fiictors can be influenced by an SCI, it is reasonable to analyze service factors that are rooted in logistics. The actual level of s is determined by conjoining performance and importance of the named service factors. The subsequent approach is applied to the fuzzy performance measurement method for supply chains, introduced by Chan et al (2003) and improved by Theeranuphattana Tang (2008). 

There are statistical procedures available to determine whether the data can be fit to a model of dose-response curves that are parallel with respect to slope and all share a common maximal response (see Chapter 11). In general, dose-response data can be fit to a three-parameter logistic equation of the form... 

Gel filtration chromatography has been extensively used to determine pore-size distributions of polymeric gels (in particle form). These models generally do not consider details of the shape of the pores, but rather they may consider a distribution of effective average pore sizes. Rodbard [326,327] reviews the various models for pore-size distributions. These include the uniform-pore models of Porath, Squire, and Ostrowski discussed earlier, the Gaussian pore distribution and its approximation developed by Ackers and Henn [3,155,156], the log-normal distribution, and the logistic distribution. 

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