John Carlin | Searcharxiv

arXiv2026

Towards Formalising Stakeholder Context using SysML v2

This paper presents a framework to bridge the gap between subjective stakeholder context and formal system architecture. This is achieved using Soft Systems Methodology (SSM) and Systems Modelling Language version 2 (SysML v2). The methodology utilises the precision of Kernel Modelling Language (KerML) and the alignment of SysML v2 with ISO 42010 to define a reference architecture for the mapping of SSM outputs to SysML v2 concepts such as stakeholders and concerns. Application of the framework is demonstrated through the use of a case study, highlighting the traceable path from stakeholder context to system architecture. The structured mapping and increased semantic precision of SysML v2 are anticipated to reduce the risk of misinterpretation compared to less formal approaches, though empirical validation across diverse stakeholder contexts remains as future work. The primary identified trade-off is the increased barrier to entry associated with SysML v2's textual notation.

cs.SE↗

arXiv2013

What Is Meant by "Missing at Random"?

The concept of missing at random is central in the literature on statistical analysis with missing data. In general, inference using incomplete data should be based not only on observed data values but should also take account of the pattern of missing values. However, it is often said that if data are missing at random, valid inference using likelihood approaches (including Bayesian) can be obtained ignoring the missingness mechanism. Unfortunately, the term "missing at random" has been used inconsistently and not always clearly; there has also been a lack of clarity around the meaning of "valid inference using likelihood". These issues have created potential for confusion about the exact conditions under which the missingness mechanism can be ignored, and perhaps fed confusion around the meaning of "analysis ignoring the missingness mechanism". Here we provide standardised precise definitions of "missing at random" and "missing completely at random", in order to promote unification of the theory. Using these definitions we clarify the conditions that suffice for "valid inference" to be obtained under a variety of inferential paradigms.

stat.ME↗