Methods of handling and working with missing/censored data (part-2)

Description As discussed in my last blog here,┬ámissing data in big data analysis cannot always be ignored and requires a good understanding of the data and user decisions on how to handle this scenario. In biology, this generally occurs when the data is subjected to limits of detection or quantification (censoring or truncation mechanism). These... Continue Reading →


Regression & Finite Mixture Models

I wrote a post a while back about Mixture Distributions and Model Comparisons. This post continues on that theme and tries to model multiple data generating processes into a single model. The code for this post is available at the github repository. There were many useful resources that helped me understand this model, and some... Continue Reading →

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