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

learn more… | top users | synonyms

1
vote
0answers
27 views

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 ...
5
votes
2answers
138 views

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 $L$ = ...
1
vote
0answers
31 views

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 ...
1
vote
0answers
22 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 ...
1
vote
0answers
9 views

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 ...
0
votes
0answers
25 views

implementing Lasso with BIC

Do you have an R code to implement Lasso with BIC? Note that there is an R package called ...
1
vote
1answer
28 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 ...
0
votes
1answer
237 views

Step function in R for regression modeling

I have to implement a regression model and i have about 30 variables in the model. Some variables does not have much influence on the model, but i need to use a formulized method for eliminating ...
1
vote
1answer
99 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 ...
1
vote
0answers
310 views

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 ...
2
votes
1answer
346 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 ...
4
votes
2answers
164 views

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 ...
2
votes
1answer
136 views

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: ...
27
votes
3answers
565 views

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 ...
3
votes
5answers
605 views

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 ...
1
vote
0answers
58 views

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?
5
votes
2answers
637 views

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 ...
0
votes
0answers
77 views

AIC and BIC for Support Vector Machine inside e1071

After training a support using the e1071 package of R, how can I calculate an information criterion such as AIC or BIC?
1
vote
1answer
53 views

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 ...
1
vote
0answers
75 views

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 ...
0
votes
0answers
619 views

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 ...
0
votes
1answer
197 views

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 ...
1
vote
0answers
36 views

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 ...
1
vote
2answers
385 views

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 ...
8
votes
1answer
378 views

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: ...
3
votes
1answer
118 views

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 ...
0
votes
1answer
55 views

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 ...
2
votes
0answers
229 views

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 $D$ ...
1
vote
1answer
174 views

comparing AIC (or BIC or whatever) between different SETS of models

Suppose I have $m$ competing models. Suppose also that I could classify these models into $s$ sets. For example, I could classify models of migration behavior conditional on climatic conditions by the ...
0
votes
0answers
210 views

AIC vs BIC vs MDL

I am trying to learn the difference between the three approaches and their applications. a) As I understand, AIC = -LL+K BIC = -LL+(K*logN)/2 Unless I am ...
2
votes
2answers
178 views

AIC, BIC parsimony

I've set up code to give me a graphical depiction of AIC vs BIC parsimony over various degrees of polynomial models. On the rare occassion AIC does not match BIC trends, which parsimonious model ...
1
vote
1answer
92 views

Sample size in BIC

The definition of the Bayesian information criterion is usually given as $BIC = -2 \text{ln}(L) + k\text{ln}(n)$, where $ln(L)$ is the maximized log-likelihood of the data given a particular model, ...
0
votes
1answer
437 views

AIC, BIC, DIC, model selection criteria

I am trying to understand the difference between these parameters, and their application. Was hoping to get some correction/clarification to my statements. I have a training set and cross-validation ...
3
votes
1answer
1k views

K-means & BIC (to validate clusters) in R

I'm wondering if there is a good way to calculate the clustering criterion based on BIC formula, for a k-means output in R? I'm a bit confused as to how to calculate that BIC so that I can compare it ...
4
votes
2answers
198 views

Two simple or one complex model, BIC and likelihood

I have a set of data points with a total number of Nt. I know a priori that the data comes from two distinct processes (distributions). I am trying to find the optimal model parameters together with ...
4
votes
1answer
225 views

Comparison between MDL and BIC

I'm currently studying Hidden Markov Models. There's a set of observations from which I need to determine the optimal number of states. After having found the maximum likelihood using Baum-Welch, I ...
1
vote
0answers
156 views

Calculating the BIC for multidimensional, clustered data

I have some microarray data (~15 samples) which I've clustered via pam, with a range of cluster sizes and I want to find out the optimal k with BIC. I basically want to re-implement the BIC score ...
1
vote
1answer
472 views

Negative and positive AICc/BIC for two models with transformed data - how to compare?

I am using AICc for model selection for transformed data (continous variable). One model I used $\log_{10}(PWV)$ as response and the other $\log(PWV)$ as response, but I am not sure which one to use ...
7
votes
0answers
166 views

AIC/BIC: how many parameters does a permutation count for?

Let's say I have a model selection problem and I am trying to use AIC or BIC to evaluate the models. This is straightforward for models that have some number $k$ of real-valued parameters. However, ...
4
votes
2answers
236 views

Can AIC determine which data better fit the same model?

Many moons ago, I asked how to differentiate between two very similar non-linear fits and which was better. Finally got that all straightened out after many headaches and three different software ...
4
votes
1answer
325 views

What to choose from BIC/AIC/ridge/elastic net?

I have the following regression problem I have about 60 independent variables; some of them have a high correlation with others. I have around 3 million observations (1) - My main goal is ...
3
votes
1answer
414 views

Hypothesis test for odds ratios

I have two possible exposure variables (A and B) for use in a statistical model predicting a binary health outcome. I have fitted models with each variable separately and now know that one variable ...
1
vote
1answer
471 views

How to calculate absolute fit indices (RMSEA, GFI…) from relative ones (AIC, BIC…)?

I have conducted an IRT analysis with Conquest in order to compare two models (1-dimensional vs. 8-dimensional) applied to a given data set (41 items of a questionnaire, N=195). Comparing the ...
13
votes
3answers
5k views

AIC & BIC number interpretation

I am looking for examples of how to interpret AIC (Akaike information criterion) and BIC (Bayesian information criterion) estimates. Can negative difference between BICs be interpreted as the ...
5
votes
1answer
961 views

BIC or AIC to determine the optimal number of clusters in a scale-free graph?

I am currently trying to partition a scale-free ("big") graph (around 20k vertices, 500k edges) into appropriate sub-graphs. Having derived the Laplacian of the graph, I tried running an approach ...
1
vote
1answer
1k views

Interpreting AIC and BIC fit

I'm writing a CFA paper, and I have run into some trouble interpreting the AIC and BIC. This is my first paper using continuous variables, thus the first time I will be reporting these fit statistics ...
1
vote
1answer
186 views

How to specify the unit information prior

Continuing on from this question and this question re BIC and its approximation to the Bayes factor with a unit information prior (Kass & Wasserman, 1995), I'm trying to quantify this relationship ...
3
votes
1answer
174 views

The unit information prior and its BIC approximation

Just moments ago, I asked this question because I've been reading Wagenmakers 2007. I now have a better understanding of what a unit information prior is and can push my knowledge further with two (by ...
3
votes
1answer
491 views

What is a “Unit Information Prior”?

I've been reading Wagenmakers (2007) A practical solution to the pervasive problem of p values. I'm intrigued by the conversion of BIC values into Bayes factors and probabilities. However, so far I ...
14
votes
1answer
1k views

Is it possible to calculate AIC and BIC for lasso regression models?

Is is possible to calculate an AIC or BIC values for lasso regression models and other regularized models where parameters are only partially entering the equation. How does one determine the degrees ...