Tag Methodology

Understanding Mutual Assent vs. Unilateral Nomination in Social Network Analysis

In the realm of social network analysis, understanding how relationships are formed and reported is paramount. The research conducted by Francis Lee and Carter T Butts delves deep into this area, specifically into the concepts of mutual assent and unilateral… Continue Reading →

Understanding Seemingly Unrelated Regression Models and Robust Inference

In the world of statistics and data analysis, understanding how to draw valid conclusions from complex datasets is crucial. Among the various methods available, seemingly unrelated regression (SUR) models have emerged as useful tools for analyzing multiple, related regression equations…. Continue Reading →

Lurking Variables Unmasked: Harnessing Dimensional Analysis for Accurate Detection

In the intricate world of data analysis, hidden factors, often termed *lurking variables*, play a critical but elusive role. Addressing these variables can illuminate otherwise obscured insights into various engineering and scientific phenomena. A recent study by del Rosario, Lee,… Continue Reading →

Transforming Time Series Analysis: A Deep Dive into Approximate Fractional Gaussian Noise Models

Time series analysis is a pivotal method used across various fields, from finance to environmental science, to model and predict behaviors over time. A particularly fascinating concept within this realm is Fractional Gaussian Noise (fGn), a model that exhibits long… Continue Reading →

Unveiling Statistical Topology: Replicating CMB Non-Homogeneity through Topological Data Analysis

In the era of Big Data, uncovering patterns and structures within vast and complex datasets presents a significant statistical challenge. A pioneering approach to address this challenge is Topological Data Analysis (TDA), which aims to offer topologically informative insights into… Continue Reading →

Fractional Gaussian Noise: Understanding Prior Specification and Model Comparison

Fractional Gaussian noise (fGn) is a crucial concept in the field of stochastic processes, particularly in modeling anti-persistent or persistent dependency structures within time series data. This article delves into the research conducted by Sigrunn Holbek S∅rbye and H∅vard Rue,… Continue Reading →

The Lasso Estimator and Improved Oracle Inequalities: A Breakthrough in High-Dimensional Linear Models

Complex data often requires sophisticated statistical models to extract meaningful insights and predictions. In the world of high-dimensional linear models, where the number of predictors exceeds the number of observations, a powerful tool called the Lasso estimator has gained significant… Continue Reading →

© 2024 Christophe Garon — Powered by WordPress

Theme by Anders NorenUp ↑