Checking that models adequately represent data is an essential component of applied
statistical inference. Ecologists increasingly use hierarchical Bayesian statistical models in …

Background Examining residuals is a crucial step in statistical analysis to identify the
discrepancies between models and data, and assess the overall model goodness-of-fit. In …

An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …

Examining residuals, such as Pearson and deviance residuals, is a primary method to
identify the discrepancies between models and data and to assess the overall goodness-of …

Introduction Accurate in-hospital mortality prediction following percutaneous coronary
intervention (PCI) is crucial for clinical decision-making. Machine Learning (ML) and Data …

Accurate model performance assessment in survival analysis is imperative for robust
predictions and informed decision-making. Traditional residual diagnostic tools like …

In microbiome research, it is often of interest to investigate the impact of clinical and
environmental factors on microbial abundance, which is often quantified as the total number …

Traditional tools for model diagnosis for Generalized Linear Model (GLM), such as deviance
and Pearson residuals, have been often utilized to examine goodness of fit of GLMs. In …

With the increasing availability of large multivariate datasets, modern parametric statistical
models makes increasing use of high-dimensional parameter spaces to flexibly represent …

Frailty models are commonly used for analyzing clustered survival data and accounting for
unobserved heterogeneity. Shared frailty models are random-effect models in which the …