【Changepoint Detection with Outliers Based on RWPCA】 Full article: https://lnkd.in/gB7nHw72 (Authored by Xin Zhang, et al., from Changchun University of Science and Technology, China.) In changepoint analysis, the outliers affect the #changepoint detection process, sometimes leading to the misinterpretation of outliers as changepoints. Therefore, it is important to consider the influence of outliers on the changepoint detection. This paper proposes a multivariate changepoint detection method which is based on the robust WPCA projection direction and the robust RFPOP method, RWPCA-RFPOP method, to detect mean changes in multivariate data, particularly in the presence of outliers, noise, and high correlations between data variables. #Double_Robust #Outlier_Detection
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Marketing Scientist | Marketing Data Science, AI, Bayesian enthusiast
In the past, the go-to solution for regression problems was to include as many variables as possible to avoid bias. This approach was common in early Marketing Mix Modeling (#MMM), where the goal was to minimize the risk of confounders skewing results. Today, I'm driven by the need to go beyond just adding variables. I'm passionate about causal discovery and creating open-source methods that help us identify authentic relationships in data, even when confounders aren't directly visible. Working with the PyMC Labs team, we've been on a mission to refine regression models through causal inference and experimentation, aiming to uncover the fundamental causal dynamics at play. Not long ago during a webinar, Aleksander Molak asked how we might integrate findings from RCTs or quasi-experiments into MMM. Now, thanks to the collective efforts of Will Dean, Dr. Juan Camilo Orduz, and the wider PyMC community, we're making significant strides in answering that question. The lines between Causality, Bayesian Inference, Structural Causal Models, DAGs, and Data Generating Processes (DGPs) are increasingly blurred. I've always believed that Bayesian methods are fundamentally causal, and our recent work solidifies this connection. Big kudos to Dr. Juan Camilo Orduz who put up this great piece of collaborative work at PyMC-Marketing. I'm excited to be part of this journey, collaborating with brilliant minds to push the boundaries of what's possible in bayesian causal inference.
A couple of months ago, I did a PyMC implementation (https://lnkd.in/dRdWYZhW) of the recent paper paper “Media Mix Model Calibration With Bayesian Priors”, by Zhang, et al. (https://lnkd.in/dp6vVgRr), where the authors describe how to parametrize and set priors on ROAS to calibrate a media mix model with lift test results. In this new PyMC Labs blog post (and example notebook), I show that the same simulation results can be recovered with the PyMC-Marketing lift-test integration strategy through custom likelihoods. This approach is more flexible as it enriches the estimates with more lift tests and different media spending to understand the saturation effect better. https://lnkd.in/dptfyr5P This is the result of the great work of Will Dean, Carlos Eduardo Trujillo Agostini, and the rest of the PyMC-Marketing developers! #mmm #bayes #calibration #pymc
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