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Bayesian data analysis in empirical software engineering---The case of missing data

Kapitel i bok
Författare Richard Torkar
Robert Feldt
Carlo A. Furia
Publicerad i Contemporary Empirical Methods in Software Engineering
ISBN 978-3-030-32488-9
Förlag Springer International Publishing
Publiceringsår 2020
Publicerad vid Institutionen för data- och informationsteknik, Software Engineering (GU)
Språk en
Länkar https://arxiv.org/pdf/1904.00661.pd...
Ämnesord bayesian data analysis
Ämneskategorier Programvaruteknik, Statistik, data- och systemvetenskap

Sammanfattning

Bayesian data analysis (BDA) is today used by a multitude of research disciplines. These disciplines use BDA as a way to embrace uncertainty by using multilevel models and making use of all available information at hand. In this chapter, we first introduce the reader to BDA and then provide an example from empirical software engineering, where we also deal with a common issue in our field, i.e., missing data. The example we make use of presents the steps done when conducting state of the art statistical analysis. First, we need to understand the problem we want to solve. Second, we conduct causal analysis. Third, we analyze non-identifiability. Fourth, we conduct missing data analysis. Finally, we do a sensitivity analysis of priors. All this before we design our statistical model. Once we have a model, we present several diagnostics one can use to conduct sanity checks. We hope that through these examples, the reader will see the advantages of using BDA. This way, we hope Bayesian statistics will become more prevalent in our field, thus partly avoiding the reproducibility crisis we have seen in other disciplines.

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