Journal ArticleDOI
The Bayesian information criterion: background, derivation, and applications
Reads0
Chats0
TLDR
The conceptual and theoretical foundations for the Bayesian information criterion are reviewed, and its properties and applications are discussed.Abstract:
The Bayesian information criterion BIC is one of the most widely known and pervasively used tools in statistical model selection Its popularity is derived from its computational simplicity and effective performance in many modeling frameworks, including Bayesian applications where prior distributions may be elusive The criterion was derived by Schwarz Ann Stat 1978, 6:461-464 to serve as an asymptotic approximation to a transformation of the Bayesian posterior probability of a candidate model This article reviews the conceptual and theoretical foundations for BIC, and also discusses its properties and applications WIREs Comput Stat 2012, 4:199-203 doi: 101002/wics199read more
Citations
More filters
Journal ArticleDOI
A dose of nature: Tree cover, stress reduction, and gender differences
TL;DR: In this article, the Trier Social Stress Test (TSST) was used to induce psychological stress, and participants were then randomly assigned to view one of ten, 6-min, 3-D videos of neighborhood streets.
Journal ArticleDOI
Variable selection strategies and its importance in clinical prediction modelling.
TL;DR: The importance of including appropriate variables, following the proper steps, and adopting the proper methods when selecting variables for prediction models is focused on.
Book
Workload Modeling for Computer Systems Performance Evaluation
TL;DR: Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system.
Journal ArticleDOI
Land-atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity.
Sha Zhou,Sha Zhou,A. Park Williams,Alexis Berg,Benjamin I. Cook,Benjamin I. Cook,Yao Zhang,Stefan Hagemann,Ruth Lorenz,Sonia I. Seneviratne,Pierre Gentine +10 more
TL;DR: It is empirically demonstrated that strong negative coupling between soil moisture and vapor pressure deficit occurs globally, indicating high probability of cooccurring soil drought and atmospheric aridity.
Journal ArticleDOI
Sex differences in thickness, and folding developments throughout the cortex
A. Kadir Mutlu,Maude Schneider,Martin Debbané,Deborah Myriam Badoud,Stephan Eliez,Marie Schaer +5 more
TL;DR: Investigation of cortical thickness maturation between the age of 6 to 30 years, using 209 longitudinally-acquired brain MRI scans found a statistically significant higher rate of cortical thinning in females compared to males, interpreted as a faster maturation of the social brain areas in females.
References
More filters
Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Book
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
TL;DR: The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Proceedings Article
Information Theory and an Extention of the Maximum Likelihood Principle
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.