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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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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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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27 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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60 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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80 views

how to decide which logistic regression model is better?

I have the following 3 models: ...
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52 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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30 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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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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38 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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34 views

Is high AIC a bad feature of the model?

I have a model with AIC equal to 78 809. Does this mean this is a very bad model or the intepretation should be different? There are 15 variables, 2-level response variable and 40 000 rows. ...
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17 views

Generalized Estimating Equations- How many predictor variables are too many based on sample size?

I am conducting analyses on wild animals, on how diet of an individual changes based on environmental changes. I will list the setup of my dataset below. Until now, I have been running independent ...
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31 views

Definition of AIC in ARIMA() function in R?

I wonder how the Arima() function in R computes the AIC. Applying the standard formula AIC= 2*k - 2 LN(L) (with k number of parameters and L maximized value of likelihood) doesn't reproduce the ...
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33 views

A guide to regularization

I'm looking for some sort of guideline as when it is appropriate to use which forms of regularization and a comparison of the advantages / disadvantages of the various forms. So something that ...
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52 views

Structural equation modelling: model selection

I am currently trying to fit a structural equation model in R with the Lavaan package. I have this model that fits my data pretty good. This model is what I consider the full model, it has all paths ...
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49 views

AIC rankings: Why would a global model rank lower than an intercept-only model?

I'm working with some real-world (i.e. potentially messy) data on the nesting ecology of several bird species. I'm attempting to relate the daily survival rate of nests to vegetation characteristics ...
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51 views

Acceptable values for variance, aic and bic in multilevel models

I'm building a multilevel model from a sample of 820 observations at level 1 and 11 groups (level 2). I'm using stata xtmixed. Running the empty model (including ...
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23 views

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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32 views

Gamma vs tweedie distribution for large productivity dataset

I'm running some GAMs using the mgcv R package on a dataset with ~8.5k observations, where productivity is the response and environmental conditions are the covariates. However I am unsure of which ...
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53 views

Multiple linear regression, backward selection : Normality of the residuals?

I need to create a Multiple Linear regression model on those data explaining max03 T9 T12 T15 Ne9 Ne12 Ne15 Vx9 Vx12 Vx15 maxO3v !My data 1 My first intuition was to make a backward selection : ...
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“object 'logl.H0' not found” - Error in fitMeasures when calculating AIC for Lavaan model

EDIT: SOLVED The problem seems to have been an explanatory variable that was a factor. If it is made binary numeric insted, the values of BIC and AIC is calculated alright. However, the analyses give ...
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48 views

Akaike information criterion for Cox proportional hazard models

I am conducting an analysis of survival data using Cox proportional hazard (CPH) models, to figure out what is the best model to use. The models I am comparing are non-nested. My plan is to compute ...
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16 views

transforming of standardised effect size in MuMIN package

I ran the following model ...
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157 views

Should auto.arima in R ever report a model with higher AIC, AICC and BIC than other models considered?

I have used auto.arima to fit a time series model (a linear regression with ARIMA errors, as described on Rob Hyndman's site ) When finished - the output reports that the best model has a (5,1,0) ...
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14 views

AIC or similar selection techniques for Variograms?

I have a very basic question: how does one choose the "best" variogram? It is possible to fit different models to an empirical variogram, e.g. nugget, ...
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73 views

Assuming a probability density for MLE to do model selection

Motivation: I am trying to use Akaike Information Criterion to assess model ranking and over-fitting risk for a set of nonlinear models. I am an electrical engineer with no formal statistical training ...
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68 views

Model averaging with MuMIn. What's the mean of pvalue?

the summary() of a model.avg made with MuMIn in R, give a lot of interesting results, in particular model averaged coefficients (estimate, standard error, adjusted standard error and a z value with a ...
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G and R matrices in mixed model and model selection

I have data in which the plants were subjected to four conditions and measured weekly for a month. I would like to incorporate "plot" as a random factor into my linear mixed model using SPSS. I am ...
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44 views

Can we use the AIC values to compare a hurdle poisson model to a multinomial logit model?

I estimated two different models using an SP survey: Hurdle poisson and multinomial logit with 5 alternatives. My dependent variable is the number of weekly trips (0,1,2,3,4,5 trips) that students ...
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8 views

Relative variable importance for simple model set

I am evaluating models based on AIC. I started by running the simplest models and the dot model (no covariates) is the best model, with little support for any others. When reporting the relative ...
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9 views

multimodel inference when using rms package

I would be glad to have some advise about how to proceed with multimodel inference to obtain weighted estimates based on AICc after running ordinal logistic analyzes with "rms' package. I used the ...
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47 views

Model averaging when linear and quadratic effects are modeled in a global model

I am trying to derived estimates of model-averaged parameter effects on a fairly complicated set of models using an information-theoretic approach. I have several models that investigate continuous ...
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22 views

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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Diagnostics for logistic regression and how to include/interpret interactions between categorical and continous variables?

I am working on a project that aims to identify the factors that affect the probability of detecting targets placed in different habitats in aerial photographs. I have done a lot of reading concerning ...
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AIC: relative versus absolute predictive error

I've read two interpretations of Akaike's Information Criterion (AIC) that seem to be in conflict, and I was hoping that someone could help me understand how to reconcile them. Interpretation 1: ...
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114 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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233 views

comparing non nested models with AIC

say we have to glmms mod1 <- glmer (y ~ x + A + (1|g) data= dat) mod2 <- glmer (y ~ x + B + (1|g) data= dat) These models are not nested in the usual sense ...
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46 views

Calculation AIC without loglikelihood-function

I want to calculate the AIC without calculating the loglikelihood-function (which seems complicated). If the residuals are normally distributed, this can be done, according to wikipedia, as follows: ...
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55 views

AIC criterion: definition

I have two questions regarding the AIC criterion : AIC=$2k-2ln(l)$ Where does the number 2 comes from? As we usually minimize it why don't we consider only : $k-ln(l)$. (Maybe I am missing ...
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25 views

model comparison when alternatives are not all nested within one another

I am running a glmm with three fixed effects: opponent 1 size ("1") opponent 2 size ("2") opponent 1 size - opponent 2 size ("diff") I am unable to run all three variables in the model at once ...
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how to extract AIC(Akaike's Information Criterion) in LAR(Least Angle Regression) in R Studio?

I'm already done in conducting the whole LAR Algorithm using lars() function in R Studio. But my problem is how to extract or use AIC in R Studio for choosing enough the number of variable that will ...
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43 views

Comparing two models

I am interested in comparing two logistic regression models. The two models are nested: model 1 contains all predictors, and model 2 contains all predictors except 1. My goal is to test if removing ...
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41 views

the role of AIC versus p-values in model selection

Let's say you are trying to choose between two models. One has two significant fixed effects. The other includes only one of the two fixed effects from the aforementioned model but has a lower AIC ...
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48 views

Model selection using mean AIC for very huge data sets

I want to select a model which best performs for a very huge data set. However, the data set is too large to calculate a model within reasonable time. If this is the case, is the following a ...
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43 views

Choosing between two parameters in a model

I have a few parameters that are related (let's call them X1 and X2), and I want to use whichever one will provide the strongest model. The model has many other parameters. Would I simply be able to ...
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1answer
49 views

AICc and K for categorical factors and interactions

I am new to multimodel inference. I am trying to create a model that has multiple categorical factors and possible interactions. For example say that my model is... Y ~ X1 + factor(X2) + ...
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1answer
56 views

Can AIC be used to compare an ARMA model to an ARMA-GARCH model?

Suppose I have one time series and two competing models that describe it. Model 1 is ARMA$(p_1,q_1)$, model 2 is ARMA$(p_2,q_2)$-GARCH$(r,s)$. I obtain AIC values of model 1 and model 2. I would ...
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Normalized likelihoods

AIC (BIC) model selection methods are widely used. These methods can select non-nested models unlike likelihood ratio type selection that requires model to be nested. The AIC has definition ...
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65 views

Prediction vs. Explanation and its Effect on Statistical Methods [duplicate]

In layman's terms, what is the difference between predicting and explaining in statistics? I was looking for the differences between AIC and BIC and found this post with an answer stating: My ...
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Number of parameters for AIC for a particular model

I know there have been a few well answered questions on this topic, but i have found myself in a bit of a special case this time. I am using AIC for model selection, and i am having trouble counting ...