Questions tagged [bic]
BIC is an acronym for Bayesian Information Criterion. BIC is one method of model comparison. See also AIC
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comparing non-nested models with different specifications based on AIc/BIC criteria
I am trying to determine if I can use the AIC/BIC criteria for model selection in the case of a multivariate probit model. I have two models with different specifications:
e.g. Model-1: mvprobit ( Y1 =...
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When does a extended BIC curve for a Gaussian Graphical model/GLasso look incorrect?
I have a model for a network, and I wanted to analyze the extended BIC curve for a graphical lasso model as according to Foygel and Drton 2010. The paper gives a list of assumptions for the data/model ...
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Negative log-likelihood, high BIC, high R-squared, low error, using a difference-in-differences (DiD) methodology [closed]
I am trying to see the impact of Brexit on UK imports. My dependent variable are EU exports to the rest of world. I have monthly data from 2013 to 2023, also data is in billions of GBP.
When I do ...
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Any Insights on the adoption and use of the Healthy Akaike Information Criterion (hAIC)?
Recently, I came across the Healthy Akaike Information Criterion (hAIC), introduced by Demidenko in his 2004 book "Mixed Models: Theory and Applications with R." Despite its (potential) ...
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BIC with non-negligible priors
I want to do model selection based on the best-fit/MAP/marginal posterior I find from an MCMC and likelihood maximization. I have a likelihood $\mathcal{L}(X|\theta)$, some informative priors $\pi(\...
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Is comparing the AIC of a Bayesian and a frequentist model right?
I’m trying to fit a general linear model where the dependant variable is a probability. It is zero-inflated and continuous, then following the advice here blog of Ben Bolker, I separated my data pool ...
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Model Fit Measures in a Binomial Logistic Regression
I am very new to regression statistics and have produced four models in the statistical package Jamovi using binomial logistic regression. Looking at model fit measures I am confused as the results ...
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What's the relationship between "bias-variance tradeoff" and "consistent model selection"?
I'm very confused about the relationship between "bias-variance tradeoff" and "consistent model selection". Based on my current interpretation, the ultimate goal of taking care of ...
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How many lags to insert into a GARCH(m,p) model?
My question might be trivial, but the doubt arises due to different ways of dealing with modeling that I have found in different research papers.
In particular, I was able to observe that (in time ...
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Why Adjusted R^2 falls if I include both individual and time fixed effects?
I have a (probably simple) question on fixed effects estimation. I am trying to do baseline growth regressions of log GDP per capita against a number of covariates and, in line with the literature, I ...
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Diebold-Mariano Revisited: what is a reasonable parameter count for information criteria when the model is complex?
Diebold (2015) wrote a follow-up paper/essay reflecting on how his work with Mariano to develop the Diebold-Mariano test has been abused over the years. One of the main points in the follow-up paper ...
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Comparing Bayesian hierarchical models with different sample sizes
I have observation data covering a certain period of time. I follow a block-maxima approach where the data are segmented into equal time intervals .My goal is to first develop a Bayesian Hierarchical ...
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Why do model selection criteria (xICs, etc) not explicitly incorporate a loss function?
Model Selection and Multimodel Inference by Burnham and Anderson notes that TIC, AIC, AICc and QAICc are based on K-L distance between a given model and true model. Also BIC is in a sense based on ...
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Is AIC or BIC preferred for prediction/explanation?
This answer (currently 89 upvotes) states:
AIC is best for prediction as it is asymptotically equivalent to cross-validation.
BIC is best for explanation as it is allows consistent estimation ...
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BIC not finding a maximum
I was trying to apply BIC to my dataset to find the ideal number of clusters and model that best fits my data, in order to use EM algorithm, but it´s not reaching a maximum, even if I increase the max ...
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