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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 =...
Jay Shah's user avatar
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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 ...
Robertmg's user avatar
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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 ...
rea123's user avatar
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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) ...
Robert Long's user avatar
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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(\...
ojima's user avatar
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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 ...
Auvray alexandre's user avatar
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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 ...
Max's user avatar
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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 ...
ExcitedSnail's user avatar
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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 ...
Giuseppe Vonella's user avatar
2 votes
1 answer
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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 ...
last_resource's user avatar
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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 ...
Dave's user avatar
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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 ...
Ahmed Bayomi's user avatar
4 votes
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27 views

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 ...
Mohan's user avatar
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3 votes
1 answer
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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 ...
Inês Pimenta's user avatar
1 vote
1 answer
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How can I rigorously quantify the increase performance due to additional parameters?

I am trying to evaluate a novel dimensionality reduction technique. Specifically, we start with a data set with around 1,000 features/covariates per observation. My technique maps this down to 12. ...
golfWolf's user avatar
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BIC model comparison [closed]

My go/nogo task was recorded in 4 blocks, each with a new set of cue images, with the learning and therefore the Q-values being reset at the beginning of each block. I have fitted different versions ...
Zahra's user avatar
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BMA formula with BIC

I am interested in using Bayesian modele averaging as a selection creteria (BMA) vs AIC. I read that BMA is widely implemented in clustering models. Suppose that we need to fit M models to a data and ...
Alice's user avatar
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AIC-BIC in mixed models

I was reading about mixed models and I am confused about AIC and BIC criteria. My first question is can I use this types to calculate them? AIC=2d-2ln(l) BIC=dln(n)-2ln(l) where d: is the numbers of ...
Superd's user avatar
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BIC to test good fitting of data to a model

I want to use the Bayesian Information Criterion in order to measure how well a gaussian and 0 order polynomial fit (using python), the one with the lowest BIC should then be the 'best fit' ? My ...
Michael's user avatar
6 votes
1 answer
382 views

Does information criteria (AIC, BIC and DIC...) imply "causality"?

I am interested in finding out the graphical causal structure. Causal Discovery algorithms (e.g., DAG learning) are used to identify potential causal graphs. In score-based causal discovery methods, ...
Jay's user avatar
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1 answer
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Maximum likelihood estimation with unknown distribution

I want to fit a model to some data using structural time series modeling and Kalman filtering. The hyperparameters need to be tuned using maximum likelihood estimation. I am using the Python package ...
eork's user avatar
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1 vote
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Which evaluation metric should I choose? AIC or MSE?

I am currently at a total loss, so I hope someone can point me in the right direction regarding my model selection. The situation I want to create a linear model that best forecasts my data. I am ...
eork's user avatar
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AIC and BIC Manual Calculations Are a Bit Off From Statsmodels Estimates in Python

I ran a multiple regression using statsmodels. I wanted to verify my understanding of calculations for log-likelihood (ll), AIC and BIC. So I attempted to manually calculate the ll, AIC and BIC for ...
GSA's user avatar
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Does anyone know how to model selection for function on function linear model to find the best subset of functional covariate in R [closed]

Does anyone know how to model selection for function on function linear model to find the best subset of functional covariate in R
Sky Sha's user avatar
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1 vote
1 answer
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BIC drop in regsubset summary different from manual calculation in R

The leaps library regsubset function gives an object that contains the list of BIC drops of each subset model from the intercept model. However, it is different from what is calculated manually. For ...
AlgoManiac's user avatar
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73 views

Exact computation of Bayes factor for multivariate normal

Question: Is there a known, exact expression for the Bayes factor between two multivariate normal hypotheses? Let $H_1$ and $H_2$ be two subsets of $R^d$ with normal priors $\pi(\mu|H_j)$. The sets $...
tims's user avatar
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1 vote
1 answer
458 views

How to report on model selection

I have run model selection and have been advised to use the BIC value to determine the best model for my purposes. However, having looked around online I cannot seem to find a standard way to report ...
Rstudioamateur's user avatar
5 votes
3 answers
808 views

Why would a smaller AIC than BIC lead to an increased chance of both overfitting and underfitting?

I am puzzled by the following statement in my lecture notes AIC penalty smaller than BIC; increased chance of overfitting BIC penalty bigger than AIC; increased chance of underfitting Is there a ...
Kirsten's user avatar
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Information criterion for cases where $n\gg k$?

I'm looking for an information criterion metric that effectively penalizes the number of parameters ($k$) in cases where we have huge sample sizes. In my particular case my sample size is $\approx80,...
Haliaetus's user avatar
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3 votes
1 answer
732 views

Why is BIC considered consistent (though AIC is mostly used) for large number of observation?

AIC, BIC are the famous criteria for model selection. But many times they show different results. I read in several places that BIC is consistent while AIC is not. And AIC can achieve minimax rate but ...
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To the likelihood of what does the loglikelihood in AIC, BIC refer?

Both Akaike's information criterion and Bayesian information criterion are calculated from the loglikelihood, eg AIC= -2 log-likelihood +2k. To the likelihood of what does it refer to? The likelihood ...
qcabepsilon's user avatar
1 vote
2 answers
732 views

How does the Bayesian Information Criterion work for model selection? [closed]

I am aware that we can use the BIC values from different models in order to determine which model predicts the data best. However, I'm a little confused about the criteria used to determine which ...
john connor's user avatar
0 votes
1 answer
67 views

Modelling predictor as quadratic term lowers BIC but marginal effect plot shows linear distribution?

I am using a modified Hosmer Lemeshaw approach, culminating in all possible combinations model selection, to conduct multiple logistic regression that should distinguish between use (1) and available ...
SageR's user avatar
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0 answers
171 views

AIC and SC tend to select the maximum lag for VECM

I am building a VAR modell in order to discover how oil price shocks and 3 macroeconomic control variables (gdp_growth, Interest rate, exchange rate) influence core and headline inflation in the USA. ...
Fritz Müller's user avatar
0 votes
1 answer
131 views

BIC values in LCA models

I have been reading posts on this forum about how to assess LCA models. One question asked about BIC statistics to assess model fit. The answer said that you shouldn't really worry about the absolute ...
May Smith's user avatar
0 votes
1 answer
37 views

Comparison problem: GAS (Generalized Autoregressive Score) and GARCH

My goal is to show that t-GAS is better than t-GARCH, so I am trying to analyze some data about Crude Oil volatility comparing these models with functions in R packages (...
Ily Del's user avatar
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0 answers
41 views

Can AIC and BIC be used to compare models trained using not nested features?

The models are trained using the same dataset. I know these criterions can be use to do model selection for nested features. Suppose model 1 has feature A,B,C, model 2 has feature B,C,D, I can use ...
StaEx_G's user avatar
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0 answers
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why in BIC Bayesian information criterion we ignore items independent of N [duplicate]

I wonder why BIC is defined in a way where items independent of N are dropped. In other words, why in the following image those in equation 6 that don't vary as N grows are ignored in equation 7. Is ...
Nicolas Fabre's user avatar
1 vote
0 answers
77 views

Comparison of factor structures using AIC and BIC

I tested using a CFA two versions of the same questionnaire: the long version (i.e., 30 items) and the shortened version (i.e., 20 items). It is possible to compare these factor structures using AIC ...
Oliver M.'s user avatar
0 votes
0 answers
178 views

Not able to calculate BIC in bidirectional stepwise regression in R

I am trying to perform stepwise regression with direction as "both" based on BIC in r. But everytime is performing based on AIC only Following is my code ...
jigar somaiya's user avatar
2 votes
1 answer
85 views

n value in the calculation of BIC

This question is building on this post, and on the general formula for BIC. BIC = kln(n) - 2ln(L) I need a bit of help understanding what the term number of ...
Joseph K.'s user avatar
1 vote
1 answer
206 views

BIC for generalized additive models

Is there any way to use BIC in model selection for gam? And if so then how to extract bic?
user avatar
1 vote
2 answers
235 views

Normalising likelihood for BIC/AIC calculation

I am running some model inference using AIC and BIC. My problem is that when I go and calculate the (maximum) loglikelihoods of my models, they are usually really high (range between 4700 and 1400 ...
ltr's user avatar
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2 votes
1 answer
168 views

How to use priors on the parameter number with an information criterion (AIC, BIC, …)?

Example The example is made up because I hope that it’s more accessible than my actual problem. I want to determine the number of planets of a star. I have: data for some astronomical observable of ...
Wrzlprmft's user avatar
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2 votes
0 answers
114 views

Is it circular reasoning to compute the ELBO using MCMC?

Let's say we have a posterior distribution $q(\theta) = p(\theta \mid D, \mathcal{M})$ over parameters $\theta$ given data $D$ and a model $\mathcal{M}$. As is often the case, computing $q$ is hard, ...
rolu's user avatar
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Difference between Bayesian Information Criteria and Approximate Bayesian Computation as model selection

My question is not very technical and more like a discussion but I will be happy to have a technical input for the comparison b/w BIC and ABC. I am trying to understand and use the best model ...
Usman YousafZai's user avatar
4 votes
1 answer
212 views

Model selection criteria that represent a compromise between AIC and BIC

I am very familiar with the ideas and formula of the two popular model selection criteria AIC/AICc and BIC. When I use them for practical problems in chemometrics, the use of AIC/AICc often gives ...
sshwang's user avatar
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1 vote
1 answer
443 views

SEM model comparison

I have problem choosing a better measurement model between two (i.e., A and B). Both models have acceptable fit indices. For example, the RMSEA of A is .056; the RMSEA of B is .067. By RMSEA, A seems ...
user359882's user avatar
0 votes
1 answer
734 views

AIC and BIC in GEE

Can I apply Akaike’s information criterion (AIC) and Quasi-likelihood under the independence model information criterion (QIC) in GEE?
Misgan L. Liben's user avatar

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