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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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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 ...
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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,...
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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?
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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 ...
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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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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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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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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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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?
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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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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 ...
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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 ...
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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 ...
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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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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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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 ...
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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 ...
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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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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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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 ...
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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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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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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?
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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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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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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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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 ...
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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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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 ...
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Comparing model fit with different number of choice occasions

I have estimated 2 models, one where each choice is modeled separately, while the other considers panel effects, thus reducing the number of choice occasions. Can I still use BIC to compare the two ...
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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, ...
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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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Statistics for information criteria of a model selection procedure

When applying a binary hypotheses testing, on an additive normal vector of measurements, assuming some regulations (similar variance) and using an information criterion of the form $$\hat{L}+P^{s}(N, ...
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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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Using BIC to determine K in K means on Grayscale and RGB image

I have used the following k-means algorithm Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids. Assign each object to ...
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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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How to derive EBIC for sparse linear regression with group lasso penalty for selecting optimum hyper parameter?

I have regression model with sparse group lasso penalty with following loss function: $||Y-\beta X||^2 + \lambda|\beta|_1 + \gamma \sum_1^g ||G_g||_2$ where $G$ is subset of $\beta$ . I have ...
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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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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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Using AIC to find a horizontal line (linear vs non-linear models)

I would like to statistically test which of the following functions is best described by a straight horizontal line (slope of 0), rather than another function, e.g. an exponential decay function or ...
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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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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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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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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 ...
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Comparing AIC and BIC of CFA and IRT

I was wondering does it make sense to compare AIC and BIC values of confirmatory factor analysis models and item response theory models? In many cases, goodness of fit indices for item response theory ...
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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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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 ...
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Residual Plots for Model Comparison

Other than using AIC and BIC, I'd like to compare two different models via graphical interpretation, therefore my question is, is it possible to compare two regression models with each one having the ...
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BIC useless as it depends on units?

The Bayesian Information Criterion (BIC) is proportional to the log of the maximised likelihood. The likelihood is a density with units given by the inverse units of the parameters. We are free to ...
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Constructing a unified path analysis model from several datasets each with different combinations of variables

I have four observational experiments (data sets) that I wish to combine and summarize in a single path analysis model. Each experiment is 3-dimensional but the observables and therefore dimensions/...