Questions tagged [aic]

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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GLMM - are there pitfalls for modeling the error distribution independently of the variable relations?

I was running a LMM with random intercepts, as such: model1 = lmer(web ~ rain + body_size + placement + canopy + understory + (1|species)) To me the residual ...
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Model with lower AIC has violated assumptions of normality and homoscedasticity

I have a repeated measures dataset with multiple plants measured every month. My DV is growth, IVs are treatment and precipitation. Since I have measured by month, growth in my data has a high ...
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AIC for a model with multiple dependent variables

I am performing a model selection analysis with some variations of the same 3-equation dynamical system of ODEs: $$ \dot{x} = f(x,y,z)\\ \dot{y} = g(x,y,z)\\ \dot{z} = h(x,y,z) $$ The particularity of ...
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Using AIC to compare mediation models with inverted IV and mediator

We have collected correlational data, and are trying to figure out which model could best explain our data. We have two competing mediation models: DV explained by A, mediation by B DV explained by ...
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How to use priors on the parameter number with an information criterion (AIC, BIC, …)?

Example The example is made up because I hope that it’s more accessible than my actual problem. I want to determine the number of planets of a star. I have: data for some astronomical observable of ...
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Understanding FE explanatory power

I am trying to understand what is going on in terms of the additional variation explained by my fixed effects. The set up is as follows. I have a a data set of roughly 3929 firm acquisition events ...
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How to validate if my ARIMA model will make reasonable forecasts

I am working on a forecasting project using ARIMA model. I've seen other related works do a train/test split on the dataset to verify their models. But I have a very small dataset which has only 60 ...
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How to apply the Jacobian correction to AIC for a transformed dependent variable when the transformation includes an independent variable?

I am comparing several OLS multivariate regression models of a dependent variable (we'll call it $Y$) using various transformations, some of which also involve one of the independent variables ($X_1$)....
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Choosing between univariate GLMMs to assess inclusion in a multivariate model

I am currently doing a project on environmental determinants of malaria vector distributions. I'm using remote sensing data for environmental variables linked via GIS. I have run univariate (binomial) ...
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Know how to choose appropriate distribution for AFT

It is written in many web resources that the distribution of the accelerated failure time model must be specified correctly but I don't think I've seen anything about how to know this. I see often ...
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Should AIC be reported on training or test data? [duplicate]

I have a handful of logistic regression models and I would like to report AIC. Should I report it on training or test data? I have quite a big dataset and a maximum of 10 predictors in any of the ...
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Comparing GAM models with/without fixed effect interactions using REML versus ML

I have several GAM models fit with package mgcv that share the same smooths and random effects groups. I would like to compare support for whether interactions ...
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corrected AIC (AICc) assumes the model is univariate?

I'm considering using the AICc instead of the AIC to select models because my sample size is not much larger than my number of parameters (n=214, K=16 - which is not enough, according to Burnham and ...
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Why Is AIC computed with a term containing -log(SSR) instead of -SSR?

Looking at https://en.wikipedia.org/wiki/Akaike_information_criterion I find the well known log likelihood $\ln\mathcal{L}(\mu,\sigma) \, = \, -\frac{n}{2}\ln(2\pi) - \frac{n}{2}\ln\sigma^2 - \...
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Compare GLM AICs with different likelihoods?

If I have a generalized linear model (GLM) with a particular likelihood, and I have another GLM of the same data (say nested within the first model), I can compare the model performance using Akaike ...
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Model selection with AIC, what to do with the selected variables

If pvalues aren't useful to look at after performing AIC variable selection (Why are p-values misleading after performing a stepwise selection?), what should be the right thing to do in a scientific ...
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AIC in Ordinal Logistic Regression Modelling

Can AIC be used to compare Unweighted and Weighted OLR Models?
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Calculate AIC for both linear and non-linear models

I have data made of vectors $\textbf{x}$ and $\textbf{y}$. I want to predict $\textbf{y}$ with $\textbf{x}$ and a set of hyperparameters $a_{1, ..., 3}$ to be fitted with a linear and a nonlinear ...
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Model selection: Is AIC enough or should one compute the p-value in model selection (and if yes to how to do it?)?

I fitted 2 models with a python package (curve_fit function from scipy.optimize) one linear and one nonlinear. I want to compare those 2 models. I compared those to model by calculating the AIC using ...
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Model selection in simultaneous ARMA-GARCH modeling without AIC [closed]

How does one determine the mean model and the variance model in simultaneous ARMA-GARCH modeling without using AIC? Rather than two step look at ACF/PACF of residuals squared of ARMA to specify the ...
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Model selection criteria that represent a compromise between AIC and BIC

I am very familiar with the ideas and formula of the two popular model selection criteria AIC/AICc and BIC. When I use them for practical problems in chemometrics, the use of AIC/AICc often gives ...
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Can a non-linear model be part of a GAM?

I am using GAMs (R package mgcv) to analyze a series of outdoor pine-needle drying studies. Varying humidity causes needles in the three treatments (tx)to lose or gain moisture (M) together; I model ...
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SEM model comparison

I have problem choosing a better measurement model between two (i.e., A and B). Both models have acceptable fit indices. For example, the RMSEA of A is .056; the RMSEA of B is .067. By RMSEA, A seems ...
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AIC and BIC in GEE

Can I apply Akaike’s information criterion (AIC) and Quasi-likelihood under the independence model information criterion (QIC) in GEE?
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AIC vs BIC for time series clustering and descriptive purposes

I'm in the process of fitting a hidden markov model with gaussian mixtures to time series health data. The primary purpose of this is descriptive, not predictive – I'm using the fitted model to give a ...
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Comparing AIC of Tensor Product Smooths versus Thin Plate Splines

I'm comparing the AIC of these two models. Tensor Product Smooth vs. Thin Plate Spline both fit using REML ...
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Assumptions in logistic regression for applying LRT and AIC criteria

I have a question. Do the following assumptions in logistic regression Linearity between the log-odds and the continuous covariates Non multicollinearity Absence of outliers need to be satisfied ...
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forward stepwise AIC approach

I am learning about performing stepwise model selection by AIC and I have 2 questions here: 1.Regarding to stepwise AIC, what is contribution (effectiveness) of the number of parameters in the model ...
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Stepwise model selection by AIC

I am learning about performing stepwise model selection by AIC and having some questions: What is the regularization parameter for step-AIC? In what way is forward step-AIC an evolution of univariate ...
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Why can't we use AIC and p-value variable selection within the same model building exercise?

In our assignment we were asked to model the compressive strength of concrete (response variable: Strength) with predictor variables Cement (kg/m^3), Water (kg/m^3), and Coarse.Aggregate (kg/m^3). We ...
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How to compare the goodness of fit between linear and logit? Why linear deviance is less than logit?

How can I evaluate which model - between linear and logit - determine the best fit to the data? The models use the same input variables and I thought that comparing the deviances was the proper choice ...
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AIC/BIC of ARIMA and ARIMA-GARCH

I was modelling a time series with an ARIMA(1,1,1) model which had an AIC of -4782.96. However, after checking squared residuals and performing ARCH tests (Engle's and McLeod-Li) I detected the ...
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Using AIC with OLS- should I use Z scores [duplicate]

I apologize at the start- I am not a statistician. The first response to my post suggested I did not phrase my question properly. I have tried editing the question to focus on my primary concern. I ...
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Options for two-stage GAM univariate model selection when AICc values <2Δ

I am constructing a two-stage GAM (stage 1 presence/absence with binomial and logit link, stage 2 abundance with poisson and log link) to model capture rate across my study site. As this is a pilot ...
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AIC between glm and gam

What are the consequences of comparing the AIC from a logistic regression model (glm) to the AIC from a generalized additive model (gam with binomial link and REML)? Here's an example of two models, ...
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AIC, BIC and log likelihood which more important?

I am currently searching for the best ARMA(p,q) model for my conditional mean. When comparing the AIC, BIC and LL, I see that some model perform better in AIC, some in BIC and some in LL. The AC and ...
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Explaining AIC Change When Removing/Adding Variables

I might be getting turned around in my thinking of AIC. My understanding, an example: Let's say I have three models: 1.) AIC = 100, it has 10 variables 2.) AIC = 100, it has 9 variables 3.) AIC = 96, ...
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Best approach for AIC model selection?

I am doing a study where I am trying to model how different factors affect polar bear movement. I would like to conduct model selection using AIC. So far, I believe I have two options:   1)    Put ...
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Can dropping an insignificant factor from a model make the model worse?

I constructed a negative binomial model for examining the relationship of 1 count variable="carid_den" on another "juv_cneb_den" (with an offset="Area_towed"), along with ...
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Paths to optimal K for GAM model selection

Let's say I have 10 different model combinations to compare via AIC for one year. There are 3 years of data, roughly 200-400 observations each year. For covariates, 2-3 of 5 appear to require tweaking ...
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Distribution of Bayesian Information Criteria - using BIC when there are multiple datasets

I have two competing nested models : $M1$ and $M2$. $M1$ has way less parameters. I know that $M2$ is definitely a true model for data (i.e., can explain data fully) but I claim that it is not the ...
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Can we compare whether different groupings of data improve model accuracy?

I have data from 100 different lab incubations of manure samples. For each sample, a 3-day incubation was done, measuring values (y-axis) against time (x-axis). I want to perform a linear regression ...
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Reconciling AICs and coefficient p values in Cox model

I’m building a Cox proportional model using two explanatory variables, A and B. The AIC for the model with only A is lower than the model with only B. This would lead me to believe that the model with ...
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Distribution comparison by AIC

I'd like to compare several distributions fitted to one dataset (of i.i.d. random variables) by AIC. Do there exist some specific rules of thumb for such a situation? It seems that most of such rules ...
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Using AIC/BIC to compare models with and without mediators

Our team ran into an issue where we had some confusion as to whether it's appropriate to use AIC or BIC to compare sets of models with and without mediators. That is, in our first model(s) we only ...
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How do you write the AIC and BIC of a regression model in terms of the coefficient-of-determination?

This question is to give a general exposition of the relationship between goodness-of-fit statistics in regression analysis, to answer questions like this one. Consider a nonlinear Gaussian regression ...
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AIC for robust generalized linear models (glm)

How can I calculate Akaike's 'An Information Criterion' with small sample size correction (AICc) for glmrob from robustbase in R?...
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In what applications do we prefer Model Selection over Model Averaging?

I'm wondering in what applications or scenarios (or in trying to answer what kind of questions), the researcher would prefer using Model Selection (such as AIC or BIC) over Model Averaging (such as ...
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What is the purpose of the model AIC if you can iteratively check the forecast RMSE at each lag?

If the purpose of the time series modelling is to build one that gives the most accurate forecast, may I ask if it necessary to check the model AIC to determine the optimal lag when you can ...
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Regression curve with lowest combined AIC and BIC is a poor predictor

For fun, I was trying to make a predictor for how long it would take for George R. R. Martin's The Winds of Winter to be released. My "best" model is the one that had the lowest combined AIC ...
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