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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On George Box, Galit Shmueli and the scientific method?
(This question might seem like it is better suited for the Philosophy SE. I am hoping that statisticians can clarify my misconceptions about Box's and Shmueli's statements, hence I am posting it here)....
2
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1answer
21 views
Can the BIC be used in neural nets despite its large no. of parameters?
I wonder if I can calculate a BIC value ($BIC=-2\log L(\hat{\theta}) + \log n \cdot d$) for a neural net. The number of parameters is large, $d \approx 20,000$ and I only have $n=700$ samples. Here, ...
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0answers
15 views
Determining the optimal number of clusters using plots of Bayesian Information Criterion
I am having trouble interpreting the results from an Expectation Maximization clustering using mclust and the Iris flower data, Using R.
Reproducible example
If ...
3
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2answers
83 views
Why is the bayesian information criterion called that way?
The word "Bayes" suggests that we are updating a distribution using data, to get a posterior distribution.
The fact that the Bayesian information criterion (BIC) is used to select a model from a set ...
0
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1answer
17 views
BIC in practice with gaussian distribution
I am considering a gaussian distribution:
\begin{equation}
y \sim N(net(x,w), \sigma^2).
\end{equation}
$net()$ is just the output of some neural net with weights $w$ and input $x$.
The log-...
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0answers
11 views
AIC criteria for a matrix decomposition problem
I am trying to decompose a matrix such that $$A \approx UV_1 \approx UV_2V_1 \approx UV_3V_2V_1V_2$$ where $A \in R^{n \times l}$, $U \in R^{n \times k_1}$, $V_1 \in R^{k_1 \times l}$, $V_2 \in R^{k_2 ...
3
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0answers
72 views
Does a high Chi square p-value for a whole model mean it is insignificant if likelihood ratio tests indicated variables should be added?
I've been estimating lots of versions of the same model by incrementally adding variables. With some variables, if I add them to the model, the likelihood ratio test indicates that they are ...
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0answers
27 views
Bayesian Information Criterion Formula Proof
while I was digging arima model I saw that BIC value is given as $k*log(n)-2*log(L)$ where $L$ is the maximized value of likelihood function whereas $k$ is number of parameters. I wonder how it is ...
1
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1answer
95 views
AIC, BIC values keep changing with lag.max in VAR model
I'm using a VARselect function from vars package in R to select order for my model. My data set has 2 time series with 21 data points.
When I give ...
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0answers
16 views
eviews augmented dickey fuller lag selection
Can someone tell me how does eviews calculate teh optimal Schwarz lag selection? I did a quick search this https://en.wikipedia.org/wiki/Bayesian_information_criterion is this the same method that ...
3
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2answers
264 views
3D plot of Akaike Information Criterion (AIC) for suitable ranges of Lˆ and k
Giving that Akaike Information Criterion (AIC) is as follow:
How can I Produce a 3D plot of AIC for suitable ranges of Lˆ and k.
In other words what could be a suitable ranges of L to try?
Moreover,...
7
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1answer
1k views
Is it possible that AIC = BIC?
Two well-known (and related) measures of model complexity from statistics are the Akaike Information Criterion (AIC) and the Bayesian Information
Criterion (BIC).
When might AIC = BIC?
7
votes
2answers
326 views
Logistic regression BIC: what's the right N?
TL;DR: Which $N$ is correct for BIC in logistic regression, the aggregated binomial or Bernoulli $N$?
UPDATES AT BOTTOM
Suppose I have a data set to which I'd like to apply logistic regression. For ...
1
vote
1answer
183 views
Find (or calculate) log-likelihood value, AIC, and BIC for SUR model (for each equation) with systemfit
I have estimated SUR model with systemfit (R package). With the estimated results, I am trying to get logLik, AIC and BIC for ...
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0answers
26 views
Inestability of BIC when selecting nested models
Currently I am working with spline regression and a method for selecting knots adaptively.
My method gives me a set of potential knots that generally has a large number of elements.
Following He et al....
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0answers
13 views
What is the prerequisites “the same dataset” for AIC/BIC?
Let make a example.
Suppose I'm doing model selection and my observation data is $Y_{N\times 1}$ and $X_{N\times K}$.(More specify, K=6)
Now I have two model, M1 and M2.
M1 includes the first ...
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0answers
78 views
Comparing AIC, BIC and HQC for selection of nested model
I am working with spline regression and in this step what I want to do is to somehow reduce the number of knots by applying backward selection.
Technically what I am doing is to delete sequentially ...
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0answers
13 views
How can I choose correct variant of ADF test?
Sorry for this question, but I am not sure in this problem. Can I make decision according to AIC, BIC and so on?
3
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0answers
35 views
A simulation study of linear mixed model
I am reading this paper, a note on BIC in mixed effects models, and I was trying to repeat their simulation study. And I will paste part of the experiment settings here to clarify my question.
Now, I ...
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0answers
26 views
Asymptotic equivalence between cross validation and bayesian information criteria
I heard that Bayesian information criteria and cross validation are asymptotically equivalent when the size of validation set is large enough similar to the relationship between Akaike information ...
1
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1answer
80 views
Understanding Intuition for ETS Damping Selection via AIC/BIC
I'm trying to understand how ETS selects whether to use a damped model via information criteria (I'm not sure which of AIC, AICc or BIC are used).
I have a time series and I'm comparing two ETS ...
2
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1answer
88 views
Selecting between OLS regression and ARIMA for time series, why AIC or BIC for ARIMA is much larger in my data?
My data set is quarterly time seires data (around 140 data points).
Method 1: simple OLS regression with 5-6 exogenous variables, which are drivers of the dependent variable. None of the explanatory ...
0
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1answer
78 views
AIC calculated in lm(y~1) and stepwise selection in R
http://www.stat.wisc.edu/courses/st333-larget/aic.pdf
The AIC calculated with the model lm(SAT~1) was 560.4736, but the AIC calculated with stepwise selection starting with lm(SAT~1) was 419.42. May ...
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0answers
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How to implement Bayesian Information Criterion (BIC) in a practical problem?
I found a lot of theoretical literature about BIC online, but I had a difficult time when I was trying to find a real case. So I was wondering how do we implement the BIC in a practical problem? Do ...
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0answers
14 views
Information criterion when model could be mis-specified and data is dependent
Common information criteria (AIC, BIC, etc) require the user to specify the likelihood function, while in practice rarely the user has the luxury to know the correct likelihood function. In the case ...
0
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1answer
131 views
How can we calculate AIC from a negative binomial GLMM?
Our problem here described is to calculate AIC from a GLMM negbin.
Our data compose by 2 Categorical variables (Yes/Not), 3 Numerical variables and our random factor, all without any NA.
We want to ...
2
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2answers
578 views
Best BIC value for K-means clusters
I am using code from Using BIC to estimate the number of k in KMEANS (answer by Prabhath Nanisetty) to find BIC values for K-means using different number of components. However, using iris dataset, I ...
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0answers
17 views
How to use a previously calculated set of clusters to start EM Clustering in R?
I have performed Hierarchical Clustering on a data set. I would now like to compare the BIC of the clustering methods. The process involves using the clusters you have determined to act as your ...
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0answers
20 views
AIC and BIC and number of quantization level
I want to test how many quantization levels (discretizing levels) are the best for the given data(time series) set I have. Therefore I am applying different levels of binning (like discretisize data ...
2
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0answers
172 views
Time series analysis VAR model: AIC and BIC test criteria
Consider two variables. Imagine you want to analyse the effects of the lags of variable A on variable B. The possiblity you see an effect of variable A on B is reasonable, but there is absolutely no ...
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2answers
353 views
What is the actual significance of a difference in AIC or BIC values?
Usually, when a difference of a statistic is discussed, that discussion is presented in the context of a significance of that difference. When self-entropy, i.e., information content, is examined, ...
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1answer
709 views
Elbow Test using AIC/BIC for identifying number of clusters using GMM
How to select number of clusters using GMM when the elbow test (AIC/BIC vs n_components) results in a graph like this?
3
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1answer
112 views
AIC and BIC in Latent class analysis
I am using the Latent Class Analysis feature available in Stata 15. The two statistical criterions gave me different indications: $AIC$ suggests me to use 6 classes, instead $BIC$ suggests to use 5 ...
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1answer
839 views
When is the AIC a good model selection criterion for forecasting and when is it not?
I'm trying to wrap my head around why the AIC and other similar ICs work as proxies for out of sample error when trying to perform automated forecast generation.
So I performed an experiment on the ...
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1answer
74 views
How to choose the number of steps ahead when comparing time series CV to the AIC or the BIC?
I would like to empirically evaluate the performance of the AIC, BIC and Cross Validation as model selection criteria for time series forecasting, i.e. which one of these criteria leads to the best ...
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419 views
Elbow Method or/and Bayesian Information Criterion to select the optimal number of clusters
I am working k-means cluster analysis. I am new in R and even newer with cluster analysis, but happy to learn.
I have managed to plot the following two figures to select the optimal number of ...
3
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1answer
101 views
As you drop variables, can AIC or BIC go up and then down?
I have some potential spline models and I'm trying to use AIC or BIC to choose variables. I'm seeing that AIC is lower when I use all variables than if I exclude any one or two. However, if I exclude ...
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0answers
54 views
Clustering with mclust and cluster packages in R [duplicate]
I need to group my data into few buckets and wanted to use clustering with R. It is important that I cluster it right as my downstream processing largely depends on how well the data is clustered ...
12
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2answers
2k views
Is it possible that the AIC and BIC give totally different model selections?
I'm performing a Poisson Regression model with 1 response variable and 6 covariates. Model selection using AIC results in a model with all covariates as well as 6 interaction terms. The BIC however, ...
0
votes
1answer
41 views
BIC estimate for multidimensional data
I want to use the Bayesian Information Criterium (BIC) to infer the number of clusters in a clustering algorithm, but I have two doubts.
Suppose I have $N$ points in a $D$-dimensional space, and that ...
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0answers
400 views
Interpreting negative vs positive BIC values
I have fit time series data to two models using SPSS. Their BIC values are as follows:
Normalised BIC, Winters: -.111, ARIMA: .048
I understand I must ...
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0answers
583 views
Correct calculation of BIC (Bayesian Information Criterion) to determine K for K-Means
I am trying to calculate BIC in python. In python, there is no inbuilt library for computing BIC. I referenced the following link to compute variance and BIC further:- Using BIC to estimate the number ...
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0answers
22 views
Significance levels vs Information criterion
Modelling financial time series data.
My mean equation was an AR(1) process. Planning to forecast the data in-sample and I have a TGARCH with IC indicating it is a better model, but the p-value for ...
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2answers
774 views
In Bayesian Information Criterion(BIC), why does having bigger N get penalized?
The Bayesian Information Criterion (BIC) is calculated with:
where RSS is resudial sum of suqares and delta squared is estimate of the variance of the error associated with each response measurement.
...
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0answers
644 views
Calculating the AICc and BIC with RSS instead of likelihood
I have found here that that the akaike information criteria, corrected for small sample sizes is:
where:
And that the likelihood can be replaced with residual sum of squares (RSS) divided by n, the ...
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0answers
13 views
Bayesian Information Criteria variable penalisation for increasing N
I am trying to understand the Bayesian Information Criteria for model performance, given by the formula below:
BIC=−2ln(L)+kln(n)
I understand the aim is to minimize this function. I can see that ...
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0answers
61 views
Deciding the optimal ARMA model
The correlogram structure of the returns series I estimated (up to two lags) had an almost identical ACF and PACF structure. The signs were identical, and the magnitudes of the ACF and PACF up to 12 ...
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72 views
Interpretation of plots for outlier detection in healthcare
Christy et al. propose cluster-based approach to outlier detection as part of the preprocessing step. However, I don't think the plots are very interpretable. The authors use the R mclustbic function ...
2
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1answer
226 views
AIC/BIC for a segmented regression model?
Can AIC/BIC be calculated reliably for a segmented regression model?
If so, can the results be compared against AIC/BIC calculations for non-segmented regression models on the same data?
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1answer
554 views
Are parsimonious models always better?
In my classes there is always some mention of AIC/BIC. Certainly model complexity is an important thing to penalize for in the sense that you don't want to include variables that are unnecessary.
Is ...
