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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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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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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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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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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34 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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Selecting the number of mixtures / hidden states / latent variables

My question applies to 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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152 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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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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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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30 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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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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34 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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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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83 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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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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48 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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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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70 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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93 views

how to decide which logistic regression model is better?

I have the following 3 models: ...
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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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34 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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137 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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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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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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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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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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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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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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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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74 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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26 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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48 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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58 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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61 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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transforming of standardised effect size in MuMIN package

I ran the following model ...
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207 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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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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78 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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94 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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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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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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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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1answer
65 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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26 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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122 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 ...