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1909. Beyond the Pearson’s correlation coefficient: a general-purpose measure for quantifying the dependency between random variables
Invited abstract in session TD-28: Advancements of OR-analytics in statistics, machine learning and data science 7, stream Advancements of OR-analytics in statistics, machine learning and data science.
Tuesday, 14:30-16:00Room: 065 (building: 208)
Authors (first author is the speaker)
| 1. | Rob van der Mei
|
| CWI | |
| 2. | Guus Berkelmans
|
| Stochastics group, CWI | |
| 3. | Joris Pries
|
| CWI | |
| 4. | Sandjai Bhulai
|
| Department of Mathematics, Vrije Universiteit Amsterdam |
Abstract
When analyzing a data set, typical questions are: ‘Do underlying relationships exist?’, ‘Are some variables redundant?’, and ‘Is some target variable Y highly or weakly dependent on variable X?’. Interestingly, despite the evident need for a general-purpose measure of dependency between RV’s, common practice of data analysis is that most data analysts use the Pearson correlation coefficient (PCC) to quantify dependence between RV’s, while it is well-recognized that the PCC is essentially a measure for linear dependency only. Although many attempts have been made to define more generic dependency measures, there is yet no consensus on a standard, general-purpose dependency function. In this talk, we will discuss and revise the list of desired properties and propose a new general-purpose dependency function provides data analysts a powerful means to quantify the level of dependence between variables.
Keywords
- Analytics and Data Science
- Machine Learning
- Forecasting
Status: accepted
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