Explores the challenges of fitting exponential models to data, including handling non-exponential growth and uncertainty in predictions.
Explains why AIC comparisons between discrete and continuous statistical models are invalid, using examples with binomial and Normal distributions.
Explores experimental cross-validation methods for complex survey data using replicate-weight decompositions to respect sampling structure.
Explains the concept of 'symbolically nested' statistical models, their computational advantages, and their importance in survey analysis.
Explains statistical methods for estimating means in small domains or subpopulations, focusing on smoothing direct estimates using models like Fay-Herriot.
Explains how to use the spatial kinetic Ising model in R to simulate changes in binary spatial patterns, like land cover.
A technical tutorial on fitting linear mixed models using pairwise pseudolikelihood in R with the svylme package, using educational survey data.
Explores using logic programming (Prolog) for data analysis, demonstrating its application on a diamond pricing dataset to build robust models.
A tutorial on performing Bayesian proportion tests for categorical survey data using R and the {brms} package.
Explains the proportional odds model for ordinal data, its assumptions, and discusses methods for testing the proportionality of odds.
Explores the intersection of multiple imputation and probabilistic record linkage, proposing a method to sample link sets for robust statistical analysis.
A tutorial on visualizing custom statistical models, including linear and non-linear fits, using ggplot2 in R for data science workflows.
A technical article explaining polynomial distributed lag models for regularization in time-series analysis, including code archaeology and R implementation.
A technical guide exploring Penalised Complexity (PC) priors for Gaussian process parameters, including theory and derivation.
A technical blog post discussing penalized complexity priors in Bayesian statistics, focusing on how to set priors that appropriately penalize model complexity.
A technical blog post discussing Bayesian priors, sparsity in high-dimensional models, and scale-mixture of normal priors for statistical computation.
Explains the theory behind linear regression models, a fundamental machine learning technique for predicting continuous numerical values.
Introducing svyVGAM, a new R package for fitting complex survey regression models using the VGAM framework with design-based inference.
A technical note on calculating denominator degrees of freedom in survey-weighted generalized linear models (svyglm) for complex sample designs.
Explores challenges in applying weighted penalized least squares to linear mixed models for survey data, highlighting estimation issues.
