Area
Statistical Modeling & Regression
72 introductions in 7 subject areas.
Linear Regression & Diagnostics8 guides
Generalized Linear Models (GLM)13 guides
Mixed & Multilevel Models (GLMM)10 guides
Multivariate Analysis11 guides
Categorical Data Analysis8 guides
Non-Parametric Statistics11 guides
Bayesian Statistics11 guides
- Simple linear regression — Advanced statistical theory
- Multiple linear regression: Complete matrix formulation
- Multiple Linear Regression: Inference and Prediction
- Advanced linear regression · Qualitative variables and interaction effects
- Linear regression: Model diagnostics and assumption validation
- Linear Regression: Influential Points and Outliers · Model Diagnosis
- Linear regression: Variation and alternative estimation methods — Advanced statistical theory
- Linear regression: Variable selection and cross-validation
- Generalized linear models: fundamentals and transition from the classical linear model
- Generalized linear models · Advanced theoretical structure
- Generalized linear models and the exponential family of distributions · Advanced reference treatise
- Generalized linear models · Parameter estimation and numerical methods
- Generalized linear models (GLMs): inference and model comparison
- Generalized Linear Models: Diagnosis, Residuals and Influence
- Generalized linear models (GLM) · Binary and binomial responses
- Generalized linear models: count data and log-linear models
- Generalized linear models for continuous non-normal responses
- Generalized linear models: quasi-likelihood and generalized estimating equations (GEE)
- Generalized linear models: structured models for correlated data
- Generalized Linear Models: Computational Aspects and Implementation
- Advanced generalized linear models · Theory and practice
- Frequentist mixed models: Introduction and foundations of correlated data
- Linear mixed models: Mathematical formulation and frequentist estimation
- Frequentist mixed models: Theory and implementation
- Frequentist linear mixed models: estimation and inference
- Frequentist mixed models: temporal and spatial correlation structures · Advanced reference treatise
- Frequentist Mixed Models: Diagnosis and Model Validation
- Frequentist mixed models: Inference and model selection
- Generalized linear mixed models (GLMM) — Advanced statistical treatise
- Nonlinear mixed models (NLMM) — Advanced theory and implementation in R
- Frequentist mixed models: Computational implementation and interpretation of results
- Multivariate analysis: Foundations of matrix algebra and multivariate data
- Multivariate analysis · The multivariate normal distribution
- Multivariate analysis: Inference and hypothesis testing · Advanced statistics
- Multivariate analysis · Dimensionality reduction - PCA
- Multivariate analysis: Factor analysis — comprehensive treatise
- Multivariate analysis: Cluster analysis (cluster analysis)
- Multivariate analysis: Classification and discriminant analysis · Advanced reference manual
- Multivariate analysis: Multidimensional scaling and correspondence analysis
- Bayesian canonical correlation analysis (BCCA) — foundations and probabilistic approach
- Multivariate analysis · Linear models and advanced structures
- Multivariate analysis · Computational implementation and practical interpretation
- Categorical data analysis and probability distributions — advanced reference
- Categorical Data Analysis: Two-Dimensional Contingency Tables
- Categorical data analysis: Introduction to generalized linear models (GLM)
- Categorical Data Analysis — Binary Logistic Regression
- Categorical data analysis: Regression models for multicategory responses
- Categorical Data Analysis: Regression Models for Count Data
- Categorical data analysis · Log-linear models for contingency tables
- Analysis of correlated and paired categorical data
- Nonparametric statistics: Foundations of nonparametric inference
- Advanced statistical inference · Nonparametric statistics for a single sample
- Nonparametric statistics · Comparison of two related samples
- Nonparametric inference · Comparison of independent samples
- Non-Parametric Statistics: Comparison of k Independent Samples
- Comparison of k related samples in nonparametric statistics
- Nonparametric statistics: Measures of association and correlation · Advanced manual
- Nonparametric statistics: Density estimation
- Nonparametric statistics: Nonparametric regression and smoothing
- Nonparametric statistics · Resampling and permutation methods
- Advanced nonparametric statistics · Advanced biostatistics
- Bayesian statistics: Philosophical and theoretical foundations of the paradigm
- Prior distributions · Advanced Bayesian statistics
- Bayesian statistics: Single-parameter models and conjugate families
- Bayesian statistics: Multiparameter models — Advanced inference treatise
- Advanced Bayesian Statistics · Inference and Modeling
- Bayesian computation methods · Treatise on modern inference
- Evaluation of model fit and model selection in Bayesian statistics
- Bayesian statistics · Linear and generalized models
- Bayesian statistics: Bayesian generalized linear models (GLMs)
- Bayesian statistics: Bayesian hierarchical models
- Advanced Bayesian inference: Theory, computational methods, and nonparametric modeling