mailitics

Tag: correlation

  • Spearman Correlation Coefficient for When Pearson Isn’t Enough

    Spearman Correlation Coefficient for When Pearson Isn’t Enough Not all relationships are linear, and that is where Spearman comes in. The post Spearman Correlation Coefficient for When Pearson Isn’t Enough appeared first on Towards Data Science. Nikhil Dasari Go to original source

  • The Pearson Correlation Coefficient, Explained Simply

    The Pearson Correlation Coefficient, Explained Simply A simple explanation of the Pearson correlation coefficient with examples The post The Pearson Correlation Coefficient, Explained Simply appeared first on Towards Data Science. Nikhil Dasari Go to original source

  • Sample completion, structured correlation, and Netflix problems

    Sample completion, structured correlation, and Netflix problems arXiv:2509.20404v1 Announce Type: new Abstract: We develop a new high-dimensional statistical learning model which can take advantage of structured correlation in data even in the presence of randomness. We completely characterize learnability in this model in terms of VCN${}_{k,k}$-dimension (essentially $k$-dependence from Shelah’s classification theory). This model suggests…

  • The Dangers of Deceptive Data Part 2–Base Proportions and Bad Statistics

    The Dangers of Deceptive Data Part 2–Base Proportions and Bad Statistics This is a follow-up to my earlier article: The Dangers of Deceptive Data–Confusing Charts and Misleading Headlines. My first article focused on how visualizations can be used to mislead, diving into a form of data presentation widely used in public matters. In this article,…

  • Scalable Geometric Learning with Correlation-Based Functional Brain Networks

    Scalable Geometric Learning with Correlation-Based Functional Brain Networks arXiv:2503.23653v1 Announce Type: new Abstract: The correlation matrix is a central representation of functional brain networks in neuroimaging. Traditional analyses often treat pairwise interactions independently in a Euclidean setting, overlooking the intrinsic geometry of correlation matrices. While earlier attempts have embraced the quotient geometry of the correlation…

  • Graph Canonical Correlation Analysis

    Graph Canonical Correlation Analysis arXiv:2502.01780v1 Announce Type: new Abstract: Canonical correlation analysis (CCA) is a widely used technique for estimating associations between two sets of multi-dimensional variables. Recent advancements in CCA methods have expanded their application to decipher the interactions of multiomics datasets, imaging-omics datasets, and more. However, conventional CCA methods are limited in their…