AIC stands for the Akaike Information Criterion, which is one technique used to select the best model from a class of models using a penalized likelihood. A smaller AIC implies a better model.

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LOOCV vs. AIC for Weighted Multiple Regression Model Selection

I am currently trying to determine the most predictive weighted multiple linear regression model to use. I don't have much formal statistical training, so I would greatly appreciate any help with the ...
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21 views

Finding AIC in a Holt-Winters Forecast [closed]

I've recently come across a problem that I can't find any information on about how to solve it. I've got set of time series data and I've tried modeling it using 9-10 different models in R, using the ...
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23 views

TIC criterion for normal regression model

I'm looking for the application of the TIC criterion in r. The TIC is an adaptation of the AIC criterion where the penalty term is replaced by the trace of the score and the Fisher information matrix ...
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36 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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24 views

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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36 views

AIC/BIC score on summary object in R

I have a fixed effects model in R where I need to use Newey-West standard errors and use the AIC score to compare models. ...
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19 views

Data Assumptions for AIC model comparisons

I recently started digging into statistical information criteria, more specifically the Akaike Information Criterion. As the literature I have read so far does not cover this, I was wondering whether ...
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54 views

Lag selection for Augmented Dickey Fuller test

Apologies in advance, I am a beginner so these questions might be quite simple. I am testing log real exchange rates for unit root stationarity for EU15 countries. I was wondering what is the best way ...
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28 views

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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17 views

Duplicate AICc values for multiple models with interactions

I am going through a model selection process with a mixed-model with 3 variables: A, B, and C. B and C are orthogonal factors. B or C may interact with A, so my full model would be: fixed: ...
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42 views

ARX model selection

I have an autoregressive model with exogenous variables: $S_{t} = \sum_{i=1}^{p} a_i S_{t-i} + \sum_q \sum_{i=1}^{r} b^q_i X^{q}_{t-i}$ where $S_t$ is the signal I want to predict and $X^q_t$ the ...
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67 views

Can you compare different functional models using Akaike criterion?

I have two regression models. One is a simple linear regression model ($y$ is regressed on $x_i$'s), while the other is a double log model (log of $y$ is regressed on log of the $x_i$'s). They have ...
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19 views

Using QIC in glm (geeglm) when there is a warning of glm.fit: fitted probabilities numerically 0 or 1 occurred

I am using geepack in R to look for the best model for my data. I run the gee function with family=binomial(link="logit"). From the results I use QIC to find the ...
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8 views

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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22 views

negative AIC or positive AIC? [duplicate]

I have calculated AIC by using R-studio to compare models. However, I got the following both negative and positive AIC AIC 8.52 0.41 -7.70 -5.84 -3.84 -2.10 Should I select the negative AIC or ...
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31 views

R: Why does step function of a Linear Modegives different AIC/BIC than AIC function?

I don't understand what I make or think wrong, but if one tries to evaluate the linear model of the data (which you can find in R in the Package AIC(stats)), then ...
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18 views

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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166 views

Logistic Regression: Bernoulli vs. Binomial Response Variables

I want to perform logistic regression with the following binomial response and with $X_1$ and $X_2$ as my predictors. I can present the same data as Bernoulli responses in the following format. ...
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36 views

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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36 views

Does AIC require the residuals of the model to be normally distributed?

Does AIC require the residuals of the models to be compared to be normally distributed?
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67 views

GLM model selection using AICc with Tweedie distribution

I have two questions regarding use of Tweedie GLM in R. I am new in using this distribution and despite a thorough search on different forums, I could not find my answers. I am now running several ...
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43 views

Comparing logistic model predictive ability with same sample but different IV

I would like to compare two logit models. Both models use the exact same sample (1 - pass a test and 0 - fail a test). The first model will use one IV (IV1) and the second model will use a different ...
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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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68 views

When AIC and Adjusted $R^2$ lead to different conclusions

I hope it's okay to ask theoretically driven R questions here. R has given me the following results from my 'tournament of models'. All models are entirely distinct except from 3 basic control ...
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46 views

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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28 views

How do I interpret which model is the best fit for my distribution (AIC)?

I'm new here so looking for some guidance; How do I interpret the following variables to understanding which model is considered the best fit to my distribution. ...
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30 views

Akaike information criterion for categorical and numerical data

How should I compute AIC for categorical and for numeric variables in classification problems? I see in Chapter 6 of Zumel and Mount that they use AIC before they train classification algorithms ...
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29 views

Alternative to AIC for feature selection in classification

I want to know what are the most common methods for feature selection in classification problems (binary and mutli-class). I see in Chapter 6 of Zumel and Mount that they use AIC before they train ...
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14 views

How to apply AIC to a situation where the mean of a multivariate normal is a 0-1 d-dimensional vector with exactly k 1's

I am trying to apply AIC to estimate mean in the following case: Let us consider that I have $n$ random variables $X_1, \ldots, X_n$, drawn i.i.d. from a normal distribution of mean $\mu\in\{0,1\}^d$ ...
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1answer
76 views

Model selection: can I compare the AIC from models of count data between linear and poisson models?

I am modeling count data (with offset / exposure parameter). My modeling strategy is use of a Poisson model and a negative binomial regression model. I compare model AICs, which are about -760 for my ...
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98 views

AIC versus cross validation in time series

I am interested in model selection in a time series setting. For concreteness, suppose I want to select an ARMA model from a pool of ARMA models with different lag orders. The ultimate intent is ...
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1answer
153 views

Selecting the number of mixtures / hidden states / latent variables

My question is regarding Gaussian Mixture models, Hidden Markov models (HMM) or any type of clustering or latent variable model, for which we can devise a likelihood function. Specifically, I train a ...
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267 views

Interpretation of (scale of) AIC, AICc and BIC when comparing different models

I'm trying to fit a model to a time series, but I am pretty confused as to which is the best. I'm looking at an arima model, and ets model and an stlf model, which each performed best within their ...
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66 views

Why would the residuals from these two models result in the wrong AIC being calculated?

We run two linear regressions, Model 1 and Model 2. The residuals from these two models are plotted against the predicted values. If I understand correctly, the AIC from these two models would be ...
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58 views

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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73 views

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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1answer
40 views

comparing $R^2$ across two data sets

I have a set of covariates that characterize the type of experience a worker has (industry experience, general management experience, etc), and I am regressing compensation on these measures of ...
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199 views

Fixed term is “(Intercept)” --Dredge function

I'm currently building a glmer model for a resource selection analysis. See code below: ...
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1answer
617 views

Stepwise Model Selection in Logistic Regression in R

I'm implementing a logistic regression model in R and I have 80 variables to chose from. I need to automatize the process of variable selection of the model so I'm using the step function. I've no ...
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1answer
51 views

How is the Akaike Information Criteron applied for model with large number of predictors?

I am reading a paper (details not very relevant) which assumes that the market cost $C$ of a trade is related to $N$ predictors $X_1,\dots,X_N$ (page 25) through a linear relationship $$C = \beta_0 + ...
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76 views

On Negative AIC Values

My question is related to the thread Negative values for AIC in General Mixed Model. I often get negative AIC values from the software I use. I notice it most when I'm doing time series. But here ...
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157 views

AIC or BIC for model selection in Cox regression (SPSS)

Does anyone know if there is a method (a macro) to calculate AIC or BIC for a model in Cox regression using SPSS?
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1answer
91 views

The best model of an AICc-based model selection on a very small sample has an high number of predictors: does it make any sense?

I'm working with a very small sample size (N=14) and I'm using AICc to identify the most parsimonious model using a large number of possible predictors. Unexpectedly the best model has six predictors! ...
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1answer
116 views

how to decide which logistic regression model is better?

I have the following 3 models: ...
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270 views

lme4: Why is AIC no longer displayed when using REML [duplicate]

I have a simple question, understanding the basic usage of the lme4 package. I am following the tutorial by Bodo Winter ...
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1answer
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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850 views

What exactly is Box-Jenkins method for ARIMA process

The Wikipedia page says that Box Jenkins is a method of fitting an ARIMA model to a time series. Now, if I want to fit an ARIMA model to a time series, I will open up SAS, call proc ARIMA , supply the ...
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Is there much point in reducing the AIC penalty for a linear model to less than 2*p?

I'm currently using a Bayesian network, which, in this instance, is the same as a bunch of linear models. The sample size I have to work with is relatively low compared to the number of parameters, ...
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1answer
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.