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Organizational mining, a sub-discipline of process mining, introduced social networks based on business processes. However, process mining has only half-heartedly pursued this line of development. In particular, the well-founded results of interdisciplinary social network analysis have hardly been taken up.
This article shows how the explanation and design of the phenomenon "organization" can benefit from the further development of this network view of business processes. To this end, the basic idea of social process networks is presented and the current state of research is outlined. Furthermore, social networks are presented as a new component of organization design. An interesting, far-reaching new field of research is expected.
This paper presents three novel findings that could be the starting point for further research, but can also be used in practice today.
We are concerned with the asymptotic theory of semiparametric estimation equations. We are dealing with estimation equations which have a parametric component of interest and a functional (nonparametric) nuisance component.
We give sufficient conditions for the existence and the asymptotic normality of a consistent estimation equation estimator for the parameter of interest.
These conditions concern the asymptotic distribution of the estimation function and of its derivative as well as the effect of the functional nuisance part in the estimation equation. In order to treat the nonparametric component we introduce a general differential calculus and a general mean value theorem. For the nonparametric part in the estimation equation we distinguish two cases: the situation of a (classical) nuisance parameter and the case of a so called working parameter.
As a special case we get regularity conditions for estimation equations with finite dimensional nuisance or working parameter. As an example we present the semiparametric linear regression model.
We consider asymptotic theory for the maximum likelihood estimator in the generalized linear model with an unknown breakpoint. A proof for the asymptotic normality is given.
The methods are based on the work of Huber (1967). The main problem is the non--differentiability of the likelihood and the score function, which requires non--standard methods.
An example from epidemiology is presented, where confidence intervals for the parameters are calculated with the asymptotic results.