mailitics

Tag: analysis

  • Roast my AB test analysis [A]

    Roast my AB test analysis [A] I have just finished up a sample analysis on an AB test dummy dataset, and would love feedback. The dataset is from Udacity’s AB Testing course. It tracks data on two landing page variations, treatment and control, with mean conversion rate as the defining metric. In my analysis, I…

  • Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis

    Gradient-based Active Learning with Gaussian Processes for Global Sensitivity Analysis arXiv:2601.11790v1 Announce Type: new Abstract: Global sensitivity analysis of complex numerical simulators is often limited by the small number of model evaluations that can be afforded. In such settings, surrogate models built from a limited set of simulations can substantially reduce the computational burden, provided…

  • A review of NMF, PLSA, LBA, EMA, and LCA with a focus on the identifiability issue

    A review of NMF, PLSA, LBA, EMA, and LCA with a focus on the identifiability issue arXiv:2512.22282v1 Announce Type: new Abstract: Across fields such as machine learning, social science, geography, considerable attention has been given to models that factorize a nonnegative matrix into the product of two or three matrices, subject to nonnegative or row-sum-to-1…

  • Geospatial exploratory data analysis with GeoPandas and DuckDB

    Geospatial exploratory data analysis with GeoPandas and DuckDB In this article, I’ll show you how to use two popular Python libraries to carry out some geospatial analysis of traffic accident data within the UK. I was a relatively early adopter of DuckDB, the fast OLAP database, after it became available, but only recently realised that, through…

  • Nonconvex Penalized LAD Estimation in Partial Linear Models with DNNs: Asymptotic Analysis and Proximal Algorithms

    Nonconvex Penalized LAD Estimation in Partial Linear Models with DNNs: Asymptotic Analysis and Proximal Algorithms arXiv:2511.21115v1 Announce Type: new Abstract: This paper investigates the partial linear model by Least Absolute Deviation (LAD) regression. We parameterize the nonparametric term using Deep Neural Networks (DNNs) and formulate a penalized LAD problem for estimation. Specifically, our model exhibits…

  • Power Analysis in Marketing: A Hands-On Introduction

    Power Analysis in Marketing: A Hands-On Introduction Part 1: What is statistical power and how do we compute it? The post Power Analysis in Marketing: A Hands-On Introduction appeared first on Towards Data Science. Sam Arrington Go to original source

  • Expected Value Analysis in AI Product Management

    Expected Value Analysis in AI Product Management An introduction to key concepts and practical applications The post Expected Value Analysis in AI Product Management appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Reduction Techniques for Survival Analysis

    Reduction Techniques for Survival Analysis arXiv:2508.05715v1 Announce Type: new Abstract: In this work, we discuss what we refer to as reduction techniques for survival analysis, that is, techniques that “reduce” a survival task to a more common regression or classification task, without ignoring the specifics of survival data. Such techniques particularly facilitate machine learning-based survival…

  • Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3)

    Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3) Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 3) appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source

  • A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation

    A Unified Analysis of Generalization and Sample Complexity for Semi-Supervised Domain Adaptation arXiv:2507.22632v1 Announce Type: new Abstract: Domain adaptation seeks to leverage the abundant label information in a source domain to improve classification performance in a target domain with limited labels. While the field has seen extensive methodological development, its theoretical foundations remain relatively underexplored.…

  • Sliding Window Informative Canonical Correlation Analysis

    Sliding Window Informative Canonical Correlation Analysis arXiv:2507.17921v1 Announce Type: new Abstract: Canonical correlation analysis (CCA) is a technique for finding correlated sets of features between two datasets. In this paper, we propose a novel extension of CCA to the online, streaming data setting: Sliding Window Informative Canonical Correlation Analysis (SWICCA). Our method uses a streaming…

  • Structural Effect and Spectral Enhancement of High-Dimensional Regularized Linear Discriminant Analysis

    Structural Effect and Spectral Enhancement of High-Dimensional Regularized Linear Discriminant Analysis arXiv:2507.16682v1 Announce Type: new Abstract: Regularized linear discriminant analysis (RLDA) is a widely used tool for classification and dimensionality reduction, but its performance in high-dimensional scenarios is inconsistent. Existing theoretical analyses of RLDA often lack clear insight into how data structure affects classification performance.…

  • Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2)

    Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2) Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python (Part 2) appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source

  • Incorporating Fairness Constraints into Archetypal Analysis

    Incorporating Fairness Constraints into Archetypal Analysis arXiv:2507.12021v1 Announce Type: new Abstract: Archetypal Analysis (AA) is an unsupervised learning method that represents data as convex combinations of extreme patterns called archetypes. While AA provides interpretable and low-dimensional representations, it can inadvertently encode sensitive attributes, leading to fairness concerns. In this work, we propose Fair Archetypal Analysis…

  • Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions

    Simulation-Based Sensitivity Analysis in Optimal Treatment Regimes and Causal Decomposition with Individualized Interventions arXiv:2506.19010v1 Announce Type: new Abstract: Causal decomposition analysis aims to assess the effect of modifying risk factors on reducing social disparities in outcomes. Recently, this analysis has incorporated individual characteristics when modifying risk factors by utilizing optimal treatment regimes (OTRs). Since the…

  • Exploratory Data Analysis: Gamma Spectroscopy in Python

    Exploratory Data Analysis: Gamma Spectroscopy in Python Let’s observe the matter on the atomic level The post Exploratory Data Analysis: Gamma Spectroscopy in Python appeared first on Towards Data Science. Dmitrii Eliuseev Go to original source

  • Riemannian Principal Component Analysis

    Riemannian Principal Component Analysis arXiv:2506.00226v1 Announce Type: new Abstract: This paper proposes an innovative extension of Principal Component Analysis (PCA) that transcends the traditional assumption of data lying in Euclidean space, enabling its application to data on Riemannian manifolds. The primary challenge addressed is the lack of vector space operations on such manifolds. Fletcher et…

  • Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations

    Generalized probabilistic canonical correlation analysis for multi-modal data integration with full or partial observations arXiv:2504.11610v1 Announce Type: new Abstract: Background: The integration and analysis of multi-modal data are increasingly essential across various domains including bioinformatics. As the volume and complexity of such data grow, there is a pressing need for computational models that not only…

  • Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm

    Generative Adversarial Networks for High-Dimensional Item Factor Analysis: A Deep Adversarial Learning Algorithm arXiv:2502.10650v1 Announce Type: new Abstract: Advances in deep learning and representation learning have transformed item factor analysis (IFA) in the item response theory (IRT) literature by enabling more efficient and accurate parameter estimation. Variational Autoencoders (VAEs) have been one of the most…

  • 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…

  • Theoretical and Practical Analysis of Fr’echet Regression via Comparison Geometry

    Theoretical and Practical Analysis of Fr’echet Regression via Comparison Geometry arXiv:2502.01995v1 Announce Type: new Abstract: Fr’echet regression extends classical regression methods to non-Euclidean metric spaces, enabling the analysis of data relationships on complex structures such as manifolds and graphs. This work establishes a rigorous theoretical analysis for Fr’echet regression through the lens of comparison geometry…

  • Inequality in Practice: E-commerce Portfolio Analysis

    Inequality in Practice: E-commerce Portfolio Analysis From Mathematical Theory to Actionable Insights: A 6-Year Shopify Case Study Image generated by DALL-E, based on author’s prompt, inspired by “The Bremen Town Musicians” Are your top-selling products making or breaking your business? It’s terrifying to think your entire revenue might collapse if one or two products fall out…

  • Non-asymptotic analysis of the performance of the penalized least trimmed squares in sparse models

    Non-asymptotic analysis of the performance of the penalized least trimmed squares in sparse models arXiv:2501.04946v1 Announce Type: new Abstract: The least trimmed squares (LTS) estimator is a renowned robust alternative to the classic least squares estimator and is popular in location, regression, machine learning, and AI literature. Many studies exist on LTS, including its robustness,…

  • Sentiment Analysis with Transformers: A Complete Deep Learning Project — PT. I

    Sentiment Analysis with Transformers: A Complete Deep Learning Project — PT. I Master Fine-Tuning Transformers, Comparing Deep Learning Architectures, and Deploying Sentiment Analysis Models Continue reading on Towards Data Science » Leo Anello Go to original source

  • The Intuition behind Concordance Index — Survival Analysis

    The Intuition behind Concordance Index — Survival Analysis The Intuition behind Concordance Index — Survival Analysis Ranking accuracy versus absolute accuracy Taken by the author and her Border Collie. “Be thankful for what you have. Be fearless for what you want” How long would you keep your Gym membership before you decide to cancel it? or Netflix if you are a series…