A technical exploration of using pairwise likelihood in linear mixed models with complex sampling, comparing results from svylme and lme4 packages.
Explores the challenges of applying signed rank tests to complex survey data and proposes a design-independent rank transformation method.
A technical discussion on the 'fourth-root' condition for estimator consistency in statistical models like GEE, exploring asymptotic theory and nuisance parameters.
Explains the core theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
A humorous analysis exploring the correlation between the number of metal bands per capita and national happiness scores in European countries.
A tutorial on visualizing mixed effect regression models and their uncertainty using non-parametric bootstrapping in R with ggplot2.
Explains why pairwise independence of variables does not imply joint independence, using a chessboard as an intuitive counterexample.
Explores the connection between the Welch-Satterthwaite t-test and linear regression using the sandwich variance estimator.
A guide to calculating marginal and conditional effects in generalized linear mixed models (GLMMs) using the R {marginaleffects} package.
Article discusses SQLite's limited built-in functions, compares it to other databases, and introduces a Go-based standard library extension.
A guide explaining marginal effects in regression analysis, including definitions and differences between types like average marginal effects, using R packages.
A technical guide explaining methods for creating confidence intervals to measure uncertainty in machine learning model performance.
A guide to creating confidence intervals for evaluating machine learning models, covering multiple methods to quantify performance uncertainty.
Analyzing chess game data from lichess.org to determine if fast thinking is the dominant factor in game outcomes across different time controls.
Discusses the practical choices in setting up asymptotic models for statistics, using examples from clinical trials and big data.
An introduction to Fisher Information, a statistical concept that quantifies how much information data samples contain about unknown distribution parameters.
Explores the mathematical and data science challenges of analyzing ordinal data, including tradeoffs in interpreting ordered scales and model limitations.
A tutorial using Euler/Venn diagrams to visualize and explain the R² statistic and variance in regression models.
A critique of publishing code as images in academic papers, highlighting errors and reproducibility issues in statistical computing examples.
A mathematical critique of additive scoring in grading and grant reviews, arguing for non-additive monotone functions.
