Connection Between OLS and Theil-Sen Estimators
A deep dive into the relationship between OLS and Theil-Sen estimators, revealing their connection through weighted averages and robust median-based slopes.
Recent posts
Exchange Rate Models: Understanding PPP and UIP
Explore exchange rate models like Purchasing Power Parity (PPP) and Uncovered Interest Parity (UIP), key frameworks in global economics.
Finite Difference Methods and the Black-Scholes-Merton Equation: A Numerical Approach to Option Pricing
Explore how Finite Difference Methods and the Black-Scholes-Merton differential equation are used to solve option pricing problems numerically, with a focus on explicit and implicit schemes.
Supply Chain Optimization and Industrial Network Analysis Using Data Science
Discover how data science enhances supply chain optimization and industrial network analysis, leveraging techniques like predictive analytics, machine learning, and graph theory to optimize operations.
Exploring Classic Linear Programming (LP) Problems and Scalable Solutions: A Deep Dive into PDLP
Linear Programming is the foundation of optimization in operations research. We explore its traditional methods, challenges in scaling large instances, and introduce PDLP, a scalable solver using first-order methods, designed for modern computational infrastructures.
Crime Analysis Using K-Means Clustering: Enhancing Security through Data Mining
This article explores the use of K-means clustering in crime analysis, including practical implementation, case studies, and future directions.
Building Linear Regression from Scratch: A Detailed Algorithmic Approach
A step-by-step guide to implementing Linear Regression from scratch using the Normal Equation method, complete with Python code and evaluation techniques.
A Guide to Regression Tasks: Choosing the Right Approach
Regression tasks are at the heart of machine learning. This guide explores methods like Linear Regression, Principal Component Regression, Gaussian Process Regression, and Support Vector Regression, with insights on when to use each.
RFM Segmentation: A Powerful Customer Segmentation Technique
RFM Segmentation (Recency, Frequency, Monetary Value) is a widely used method to segment customers based on their behavior. This article provides a deep dive into RFM, showing how to apply clustering techniques for effective customer segmentation.
The Math Behind Kernel Density Estimation
Explore the foundations, concepts, and mathematics behind Kernel Density Estimation (KDE), a powerful tool in non-parametric statistics for estimating probability density functions.