Data Science: Mindset, Methodologies, and Misconceptions by Zacharias Voulgaris PhD

Author:Zacharias Voulgaris PhD [Zacharias Voulgaris PhD]
Language: eng
Format: epub
Tags: Big Data
Publisher: Technics Publications
Published: 2017-08-31T21:00:00+00:00

Global Sensitivity Analysis Using Resampling Methods

One popular and effective way of tackling the inherent uncertainty of experimental results in a holistic manner is resampling methods (there are other methods for global sensitivity analysis, but these are beyond the scope of this book, as they are not as popular). Resampling methods are a set of techniques designed to provide a more robust way of deriving reliable conclusions from a dataset by trying out different samples of the dataset (which is also a sample of sorts). Although this methodology has been traditionally statistics-based, since the development of efficient simulation processes, it has come to include different ways of accomplishing that too. Apart from purely statistical methods for resampling, such as bootstrapping, permutation methods, and jackknife, there is also Monte Carlo (a very popular method in all kinds of approximation problems). Let’s look at each category in more detail.

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