• Madalina Ciortan

Unimodality tests and Kernel density estimations

Updated: Dec 30, 2018



When processing a large number of datasets which can potentially have different data distributions, we are confronted with the following considerations:


- Is the data distribution unimodal and if it is the case, which model best approximates it( uniform distribution, T-distribution, chi-square distribution, cauchy distribution, etc)?


- If the data distribution is multimodal, can we automatically identify the number of modes and provide more granular descriptive statistics?


- How can we estimate the probability density function of a new dataset?


This notebook tackles the following subjects:


- Histograms vs probability density function approximation

- Kernel density estimations

- Choice of optimal bandwidth: Silverman/ Scott/ Grid Search Cross Validation

- Statistical tests for unimodal distributions

- DIP test for unimodality

- Identification of the number of modes of a data distribution based on the kernel density estimation


#kerneldensity #modality

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