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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What is the point of dividing data into training and test parts to assess prediction properties when we have AIC?

Asymptotically, minimizing the AIC is equivalent to minimizing the leave-one-out cross-validation MSE for cross-sectional data [1]. So when we have AIC, why does one at all use the method of dividing ...
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AICc is picking overly complex models - something stricter?

I'd like to know if there are stricter alternatives to automated model selection than AICc / AIC / BIC. We have approximately ten thousand curves, and for each we'd like to find the most parsimonious ...
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28 views

Multiple testing & model selection using AICc

I have a situation very similar to this post. I am finding the best-fit mixed effects model among a set of 5 candidate models for 7 different dependent variables (y1 to y7) using the same dataset of ...
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11 views

AIC of a two-part/hurdle model?

I have continuous data with a point mass at zero, so my plan is to use a two-part model where I first model whether an observation is zero or non-zero in a logistic regression and then model the ...
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85 views

Using the drop1 command in R and AIC

While using the drop1 command in R for model building, it is said the variable with the lowest AIC value must be dropped. What could be the reason for the same? I know AIC talks about information loss ...
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How freqently are the information conditons for proper Akaike information criterion application actually met?

Akaike information criterion (AIC) is limited to goodness-of-fit for assumed distributions. That is, for an assumed distribution (especially homoscedastic) one can assume a maximum likelihood ...
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11 views

GLM and GLMULTI have different AIC result [closed]

I try to find the best model by using glmulti. Code and result below. So, I choose the simplest model, model 2. When I use glm ...
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1answer
29 views

ARMA model selection: in-sample vs. out-sample accuracy measures

I have a time series for 1000 days for many firms. I am interested to know, in general, on what basis I should select an ARMA model (the nature of my problem restricts integration order to 0). Should ...
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8 views

visualisation of GEEGLM results and AIC

I'd like to ask for help with geeglm function. I have been advised to perform a model (geeglm), but was not able to find very information on if 1) it is possible to plot the results of the model ...
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42 views

Why are the both of two models' AIC the same?

I would like to ask a question of AIC when we use Generalized Linear Model with R. I show you 4 my models. "x" is continuous variable. "f" is categorical variable and has two levels, C and T. "x*f" ...
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25 views

VAR model: include all lags up to AIC-suggested order or just the significant ones?

I'm building a regression model in which I have a dependent variable OSE, and two independent variables, MSCI and Brent. In this model I wish to include lagged variables. I performed an AIC for my ...
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40 views

AIC, model selection and overfitting

I am looking for references that specifically show that Akaike's Information Criterion (AIC), or its corrected form (AICc), can in some practical applications -- that is, not in the asymptotic regime ...
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12 views

Obtaining AICc weights after glm.nb

I am performing negative binomial regression using glm.nb() function from MASS package and calculating AICc using package "AICcmodavg". I need also to obtain the (AICc) weights using aictab() function ...
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28 views

Is AIC a valid criterion for the selection of variance structures in GLS?

In generalized least squares, I’ve specified a weights function that accounts for heterogeneity in residual variance that exist along the range of a covariate. The validation graphs (residuals ...
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range of possible values and model complexity

I have a prediction model where the likelihood is a function of two components $(A,B)$ $L \sim \bf{A}^\alpha + \bf{B}^\beta$ , where both $\bf{A}$ and $\bf{B}$ are $n\times n$ matrices ($n$ is the ...
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Model Averaging: Standard Error vs Adjusted Standard Error

I'm having trouble understanding when to use Std. Error or Adjusted SE in model averaging (model.avg). What/Why/When to use Adjusted SE? This is my code: ...
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30 views

AIC and nested models

There is a related question here but two highly upvoted answers there don't seem to agree with each other, and in any case I wanted to take a different tack. Is AIC useful for comparing: non-nested ...
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24 views

Different AIC values for the same SARIMA model in different R packages

I've been trying to run a simple time series model for a data set. My biggest question is when I run the auto.arima model (from package "forecast) for this data set ...
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1answer
40 views

logistic regression - class created with non significant coefficients?

I've created an example table (just in order to create a function) with: ...
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27 views

AIC on Savitzky-Golay width

I want to use a Savitzky-Golay filter to smooth some data. There is a right width to use based on the data that it is smoothing. A number of papers basically use "eyeball norm" on the parameters but ...
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Likelihood ratio test seems to show little difference between models with AICc difference of 3

I'm running a multinomial logistic regression analysis of the behavioural responses of deer to camera traps using no reaction, reaction and strong reaction as dependent variables and season, camera ...
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77 views

Variable selection vs Model selection

So I understand that variable selection is a part of model selection. But what exactly does model selection consist of? Is it more than the following: 1) choose a distribution for your model 2) ...
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Scenario of how to compare models

I have a binary classification problem where the distribution of classes is skewed. I've already trained some scoring models with logistic regression. Now I would like to compare them. How to do this ...
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Is it meaningful to compare a logit and probit model with the same no. of observations and independent variables using AIC and BIC?

For my dissertation i want to show that the logit and probit model produce the same results. So far i've compared the marginal effects, percent correctly predicted and done a scatter plot of the ...
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Does BIC try to find a true model?

This question is a follow-up or attempt to clear up possible confusion regarding a topic I and many others find a bit difficult, regarding the difference between AIC and BIC. In a very nice answer by ...
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Comparing categorical variable importance across groups; zero and one beta regression

I am attempting to compare behavioral responses across two species (one native and one invasive). Predictors run the range of types including continuous (size), discrete (day of trial) and ...
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19 views

How can a result from reduced mixed model be insignificant (with smaller BIC and AIC) when the whole model gave significant results

Hi I have made a linear mixed model with a number of covariants that could be relevant to control for. This entire model gave me a significant result. However when I reduce the model, removing the ...
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70 views

Log-likelihood ratio test vs. information criteria for model selection

I am trying to select the model with the best fit among GARCH(1,1), ARMA(2,2) and GJR-GARCH(1,1) models for a time series of log returns. The results from IC (Akaike, Bayesian) and likelihood-ratio ...
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29 views

Justification for AIC/BIC vs F-statistic when using stepwise backward elimination

I note that some stepwise backwards elimination methods use AIC to make the decision about which variables to eliminate, and others use the F-statistic. Why would I use one over the other, and is ...
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28 views

Using AICc distributions to assess goodness-of-fit and model selection

I have a couple of ordinary differential equation models that I'm trying to fit to time-dependent biological data ($y_n$). One model is more complex than then other as it has more free parameters. I ...
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39 views

Is it inherently invalid to use BIC for model averaging?

If I understand AIC/AICc vs. BIC correctly, AIC presumes that there is no "true" model and that any given model is simply a "best worst approximation". However, BIC presumes that there IS a "true" ...
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21 views

Model comparison: better AIC, but worse Mean Actual/Predicted Response

I have made two regression models using gamma regression, and fitted them to 80% of my data, with the intention of doing out-of-sample analysis on the 20%. Model 2 is just Model 1, with one extra ...
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Usage of AIC for comparing models [duplicate]

Can AIC be used to compare a model with exponential smoothing with linear regressions?
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When should I use AIC instead of cross-validation?

Both can be used to select a best model and both penalize overfitting. However, CV is easier to understand and to apply in practice. Thus, when should I use AIC instead?
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How to decide between quasi-poisson and negative binomial?

I tried quasi-poisson and negative binomial glms on my counted data in R. The estimates are pretty much the same but p-value are different. Quasi-poisson gives insignificant result. NB give ...
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1answer
77 views

Time series model selection: AIC vs. out-of-sample SSE and their equivalence

AIC is frequently recommended as criterion to compare models for time series forecasting. See for example this in the context of dynamic regression models: The AIC can be calculated for the final ...
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26 views

Threshold for AIC for fitting probability distributions to data

I'm fitting probability distributions to my dataset (using Matlab). It is continuous data (later I will probably also face discrete data). I'm ranking the distributions with the corrected AIC score. ...
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73 views

Understanding specifics of glm() model in R

I have been provided a sample logistic regression as follows: ...
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1answer
182 views

AIC versus Likelihood Ratio Test in Model Variable Selection

The software that I am currently using to build a model compares a "current run" model to a "reference model" and reports (where applicable) both a chi-squared p-value based on likelihood ratio tests ...
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Why is the AIC different between these two models?

First, let us generate some data: ...
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Higher SIC and lower S.E. of residuals

I used the Schwarz Information Criterion (a.k.a., BIC) and the Akaike Information Criterion (AIC) to select the models for a time series Analysis. AIC got me an ARMA (5,4) and SIC got me ARMA (2,1). ...
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Will AIC become smaller when we add more regressors?

As far as I know, AIC's function is similar to $R^2$, which checks fitness of a model. Will AIC become larger as we simply add more regressors on right hand side of regression? For instance adding ...
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1answer
93 views

GLM - which probability distribution to use for abundance data?

I'm fitting a generalized linear model to try to understand how the abundance of a species of freshwater fish varies in response to some environmental variables. I'm using the AIC to choose between ...
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40 views

auto.arima always produces infinite AICs for all MA models, despite being invertible [closed]

I have been fitting ARIMA models to panel data. My goal is to develop a single ARIMA model that explains growth across a number of different regions. To do this, I followed Rob Hyndman's excellent ...
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75 views

Why information criterion (not adjusted $R^2$) are used to select appropriate lag order in time series model?

In time series models, like ARMA-GARCH, to select appropriate lag or order of the model different information criterion, like AIC, BIC, SIC etc, are used. My question is very simple, why donot we ...
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10 views

Evaluate information lost of two different estimators using AIC?

I thinking about following issue. I have two type of estimators. 1) The first one is classic glm so the final rate is predicted values from glm. The second estimator use following method. At ...
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53 views

Finite maximum log-likelihood but infinite AIC (bug in R?)

I have a panel data set with binary dependent variable of 20,000 observations and 11 independent variables. I ran a logistic regression with fixed effects and the model returns maximum log likelihood ...
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102 views

auto.arima not giving the best model according to information criteria

I'm using auto.arima to get the best model for the MASS dataset deaths. However, ...
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56 views

p-values of the coefficients or AIC for model selection in multiple regression

I´ve got two models from a multiple linear regression (A and B, see below) and don´t know which to select. I want to predict a value called AW as good as possible, so I´d like to have the highest r². ...
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Post Model Selection Inference problems - which remedies exist?

Recently, Hannes Leeb from Yale University and Benedikt Pötscher from the University of Vienna have published a series of papers dealing with what they call Post Model Selection Inference problems.* ...