BIC is an acronym for Bayesian Information Criterion. BIC is one method of model comparison. See also AIC

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Implementing the Bayesian Information Criterion (BIC) Using PyKalman

I'm trying to use pykalman to do a Kalman filter on financial data and it seems to be generally working very well. However, when I attempt to extend the code using BIC $\mathrm{BIC} = {-2 \cdot ...
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Integrated Classification Likelihood computation for R package HDclassif

I'm in the process of fitting some mixture models to some data I have. As this data is high-dimensional, I used the subspace clustering package HDclassif. As the package has no option for the Akaike ...
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35 views

Reasons for EGARCH(1,1) producing higher/worse AIC/BIC than GARCH(1,1)

I am using the log returns of 3 different stock indices. Two of them show improvements in AIC/BIC critereon when I fit EGARCH(1,1) in comparison to GARCH(1,1). One does not. Assuming that estimation ...
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Comparabilty of BIC, AIC and ICL from Mclust. HDclassif and fem objects

I have a question regarding the way BIC, AIC and ICL are computed in the packages mclust, HDclassif and fisherEM. Both of these packages use the negation of AIC, BIC, ICL (bigger is better). This is ...
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BIC in Item Response Theory Models: Using log(N) vs log(N*I) as a weight

In IRT software packages and in the literature it is common to calculate the BIC as $$ \mathrm{BIC} = -2 \cdot \mathrm{logLik} + \log(N)\mathrm{Npars} $$ where $N$ is the number of rows in wide ...
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Modeling different lag structures

I know there are various information criteria that can be used to compare model specifications, including those with different lag structures. I can easily compare the Akaike Information Criterion ...
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Should information crtieria be applied to training or validation data?

Information criteria for selecting models seem to be applied to training data in general. Could they also be applied to validation data to decide the most predictive and simple model, or is this ...
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BIC for Bayesian ANOVA

I am doing a Bayesian ANOVA as follows: BIC0 = -2 * logLik0 + k0 * log(N) # null hypothesis BIC BIC1 = -2 * logLik1 + k1 * log(N) # alternate hypothesis BIC ...
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Sample size when fitting categorical survey data

I have a model which fits data from repeated surveys: at time $t$, a number $n_t$ respondents is asked a question and can give one of $K$ answers ($k=1, ..., K$). This is repeated $T$ times ($t = 1, ...
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Information criteria for ARIMA model: missing log-likelihood for null model

I am trying to fit an ARIMA model on the time series of exchange rate. I have tried several kinds of ARIMA specifications (MA(1), MA(1,2), ...) and I am evaluating the particular setting according to ...
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Sampling from a set of non-nested models

Consider a collection $\mathcal{M}$ of $m$ different model classes $\mathcal{M} = \{M_1,\dots,M_m\}$, where each model class has a parameter set $\Theta_i$, $i=1,\dots,m$. The model classes are not ...
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Multiple linear regression: does BIC drop (vaguely) collinear variables?

Say I have the following multiple linear regression: Y ~ X1 + X2 + X3 + X4 All X variables are independent, but X1 and X2 look kind of linearly related when ...
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AIC/BIC values keeps falling as I add more and more lags. How do I select the appropriate lag length?

I am trying to minimize the values of the Akaike and Bayesian Information Criteria to figure out the optimal lag structure for my ARDL error correction model. I am using Stata to run my analysis and ...
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39 views

Parameter Estimation vs Inference Error

I am having trouble reconciling (or maybe even understanding properly) the following issues: We have a dataset. We hypothesize a functional form for probability density. Then we estimate the ...
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GLMM with 2 insignificant variables has lower AIC or BIC compared to same model without those variables…?

I am having a hard time understanding what's going on in with my model selection, and why a model with two insignificant variables is getting chosen as the "best model" over a model without those two ...
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91 views

How does step function selects best linear Models which includes polynomial effects and interaction effects in R?

I try to find "best" linear models with continuous and categorical covariables with Interaction Effect by BIC. The continuous covariables should have a quadratic effect on the response variable. ...
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79 views

Automatic selection of lowest information criterion comes with warning

I am building a forecasting model (ARMA) and found the very useful code-object arma_order_select_ic(see code below). It all works, however, each calculation comes ...
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58 views

Comparing AIC or BIC for constant-only models vs ARIMA models

What if the AIC/BIC is lower (negatively speaking) with the model including just the constant with respect to other ARMA versions? I don't think because k=1 it is lower by construction.
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Applying the Bayesian Information Criterion for Stepwise Selection Algorithms on Time Series

The title sounds rather complicated for fairly simple statistics issue. I've created a factor model that tests adding additional factors by checking if the improvement in the mean squared error ...
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Excluding Outliers and Influential Observations ($R^2$ and AIC/BIC)

I am working on a cross-sectional data set relating mortgage payments to debt-income ratios. I have some extreme outliers and experimented with excluding them from the model (some 30 observations of a ...
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Selecting the right BIC value

I'm using the hddc for an assignment to find the optimal number of clusters. The dataset is 9-dimensional and consists of 200.000 rows, however, the BIC values that I'm getting are really high. How ...
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Is there a BIC or AIC formula for correcting a G-statistic?

I am using the G-test (http://en.wikipedia.org/wiki/G-test) for scoring models with different numbers of parameters in a model comparison problem. Is there a BIC or AIC formula to correct a ...
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129 views

Suitable metric for consistency of parametric models

When fitting a parametric model to a data set assuming that our selected model class contains the truth, what performance metric should be used so that parameters converge to the truth as sample size ...
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what are parameters in a model and how do I get them?

I recently asked a way to calculate BIC score for a given HMM (transition, emission, initial distribution). After doing some more research (basically the wiki page and this CV thread) I realized its a ...
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How to correctly choose model based on BIC?

I have a question about Bayesian Information Criteria. (GARCH models) I have looked for so many hours but still very confused about this BIC especially a negative one. As far as I am concerned it is ...
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AIC, BIC and GCV: what is best for making decision in penalized regression methods?

My general understanding is AIC deals with the trade-off between the goodness of fit of the model and the complexity of the model. $AIC =2k -2ln(L)$ $k$ = number of parameters in the model $L$ = ...
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Model without endogeneity correction has lower AIC than one with correction

I have two models, one with endogeneity correction (includes correction terms obtained using Heckman) and one without. The correction terms are significant in the second stage model, yet the AIC/BIC ...
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120 views

LCA number of parameters & degrees of freedom

I have a series of physicians' claims submissions. I would like to perform cluster analysis as an exploratory tool to find patterns in how physicians bill based on things like Revenue Codes, Procedure ...
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determing states in HMM with BIC

I'm fitting a HMM to time series, for each data set I use BIC results to select the optimum number of states. In that, the BIC number is lowest and thereby indicating this model with that number of ...
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112 views

implementing Lasso with BIC

Do you have an R code to implement Lasso with BIC? Note that there is an R package called ...
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98 views

Prerequisites for AIC/BIC model comparison

I have a question about model selection when using AIC/BIC. So, if two model structures are totally different, can I still directly apply AIC and BIC? Also, for a hierarchical model, how to compute ...
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296 views

Difference in AIC and BIC values between sem and lavaan packages in R

I ran the same SEM model in sem and lavaan. I got the same parameters and - generally - very close test values, with the exception of AIC and BIC which were immensely different between the two ...
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Using BIC to estimate the number of k in KMEANS

I am currently trying to compute the BIC for my toy data set (ofc iris (: ). I want to reproduce the results as shown here (Fig. 5). That paper is also my source for the BIC formulas. I have 2 ...
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839 views

AIC BIC Mallows Cp Cross Validation Model Selection

If you have several linear models, say model1, model2 and model3, how would you cross-validate it to pick the best model? (In R) I'm wondering this because my AIC and BIC for each model are not ...
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Sparse parameters when computing AIC, BIC, etc

I'm designing large-scale, regularized logistic regression models with lots of sparse, binarized features. e.g. isUS, isFR, etc. As a result, a lot of the weights in the model are zero. I'm wondering ...
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Why do I get different BIC values when I use regsubsets and lm in R

I used regsubsets to find a model with lowest BIC; height is our D.V. , the code I typed is below: ...
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AIC,BIC,CIC,DIC,EIC,FIC,GIC,HIC,IIC — Can I use them interchangeably?

On p. 34 of his PRNN Brian Ripley comments that "The AIC was named by Akaike (1974) as 'An Information Criterion' although it seems commonly believed that the A stands for Akaike". Indeed, when ...
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AIC guidelines in model selection

I typically use BIC as my understanding is that it values parsimony more strongly than does AIC. However, I have decided to use a more comprehensive approach now and would like to use AIC as well. I ...
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Bayesian justification for AIC/BIC

Can someone point me to a straightforward and comprehensible Bayesian discussion justifying AIC and/or BIC? Or even better, can someone give a self-contained such discussion in this forum?
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Correct number of parameters of AR models for AIC / BIC ?

I have a time series and want to use AIC / BIC to decide which of the following model is most appropriate: A) AR(1), no constant with Gaussian innovation term B) AR(2), no constant with Gaussian ...
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How to interpret BIC

I am fitting two different models to the same data. In one model, there is one free parameter for three different experimental conditions. In another model, I fit three free parameters, one for each ...
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BIC vs. Out of sample performance

I have two statistical models. Model 1 uses a GLM approach while model 2 uses a time series approach for fitting. I want to compare these two models. Model 1 (i.e. GLM) has a better out of sample ...
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Calculate BIC to determine the optimal number of clusters (k-means clustering)

I have a set of data and want to know whether they fall in 1, 2 or 3 groups. I started exploring the question by using k-means in MATLAB. By just looking at the distance from the centroid of each ...
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Bayesian Information Criterion (BIC) for large samples

The Bayesian information criterion is defined as $BIC = -2 \text{ln}(L) + k\text{ln}(n)$, where $L$ is the maximized likelihood of the data, and where $n$ is the sample size. In case of a huge sample ...
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Computing BIC for SUR model

Consider the following m regression equation system: $$r^i = X^i \beta^i + \epsilon^i \;\;\; \text{for} \;i=1,2,3,..,T$$ where $r^i$ is a $(T\times 1)$ vector of the T observations of the dependent ...
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Negative binomial GLM, the most complex model always has lowest AIC (all interaction terms)

I apologize for all those questions on modelling. It is the very first time that I try GLM and I am really lost even after reading a lot of papers. I have divided my covariates according to their ...
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Criteria for selecting the “best” model in a Hidden Markov Model

I have a time series data set to which I am trying to fit a Hidden Markov Model (HMM) in order to estimate the number of latent states in the data. My pseudo code for doing this is the following: ...
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Bayesian model comparison: What is it about MCMC that makes RSS or BIC hard to use?

I'm trying to figure out why certain methods are used for comparing models in Bayesian statistics. DIC is often used in Bayesian model comparison. However, I'm under the impression that one could ...
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BIC and AIC(c) and group data

I would like to compare two models using the BIC and AICc. Doing so seems fairly straightforward if both models are fit to only one dataset. However, I have data from 10 participants, and there is no ...
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Number of free parameters in Gaussian mixture models

When comparing GMM models with different number of components (i.e number of Gaussians) one penalizes the likelihood for the total number of free parameters in the mixture model. If the data is in $D$ ...