2024/06/11 by Benjamin M. Bolker · 1 voice · 8 citations
Computer Science · Environmental Science · Mathematics · #A priori and a posteriori #Artificial intelligence #Bayesian inference #Bayesian probability #Computer science #Confidence interval #Data Analysis with R #Data mining #Ecology and Vegetation Dynamics Studies #Econometrics #Estimator #Machine learning #Mathematics #Model selection #Prior probability #Regression #Selection (genetic algorithm) #Shrinkage #Soil and Water Nutrient Dynamics #Statistical model #Statistics
paper · pdf · doi:10.3390/e26060506
published in Entropy 26(6), 506 (Multidisciplinary Digital Publishing Institute)
openalex publication_date 2024/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22
Information-theoretic (IT) and multi-model averaging (MMA) statistical approaches are widely used but suboptimal tools for pursuing a multifactorial approach (also known as the method of multiple working hypotheses) in ecology. (1) Conceptually, IT encourages ecologists to perform tests on sets of artificially simplified models. (2) MMA improves on IT model selection by implementing a simple form of shrinkage estimation (a way to make accurate predictions from a model with many parameters relative to the amount of data, by "shrinking" parameter estimates toward zero). However, other shrinkage estimators such as penalized regression or Bayesian hierarchical models with regularizing priors are more computationally efficient and better supported theoretically. (3) In general, the procedures for extracting confidence intervals from MMA are overconfident, providing overly narrow intervals. If researchers want to use limited data sets to accurately estimate the strength of multiple competing ecological processes along with reliable confidence intervals, the current best approach is to use full (maximal) statistical models (possibly with Bayesian priors) after making principled, a priori decisions about model complexity.