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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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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Does a lower AIC imply better forecast RMSE for time series models?

In my application, I am using the VECM function of the tsDyn package in R to fit 4 I(1) processes. Using the Johansen's test, I have identified for 1 cointegrating relationship. I have made use of the ...
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Should I use AIC / BIC or rather cross validation for discovering gov. equations through linear regression (SINDy)?

I want to use linear regression with very large design matrix for discovery of governing equations to i.e. physical systems. The design matrix would include potential terms that can be part of the ...
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What defines an observation in AIC?

I have a question based upon a section of this paper: "Model selection for dynamical systems via sparse regression and information criteria", which can be found here: https://arxiv.org/abs/...
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Maximum Likelihood Function for computing AIC of LS regression models

In Burnham & Anderson: Model Selection and Multimodel Inference (https://link.springer.com/book/10.1007/b97636), the author states that the likelihood function used in the Akaike information ...
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How to calculate the degrees of freedom for L1 and L2 regularised GLMs?

My goal is to calculate various information criteria for generalised linear models (e.g., the AIC). To do this, we need to calculate the effective degrees of freedom of the trained model. In an ...
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Can you compare negative binomial models by AIC?

I have a question about comparing negative binomial models by AIC versus QAIC. My dependent variable is a count, and I have one random term (I am using generalised linear mixed models fitted with the ...
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ODE model selection criterion selection criteria

There is substantial literature on model selection criteria and many questions around this topic on CrossValidated. However, I could not find one that covers my case. I have a time series dataset (of ...
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How to properly count the number K in the Aikaike Information Criterion (AIC) in non-linear models?

Let's say I have a non-linear model model of the form $$\mathbf{y}(\mathbf{x})=a\cdot e^{b\cdot\mathbf{x}}$$ There are 2 parameters to fit ($a$ and $b$). In a reference book (Burnham, ed. 2002, ISBN: ...
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avoiding model overfitting when fitting parameters/models to an ordinary differential equation

I am working on fitting an ODE model to some data. So I have a vector of time series data $\textbf{x} = [x_1, x_2, ... x_n]$, and an ODE model $\dot{x} = f(x, \theta)$, where $\theta$ is a vector of ...
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Calculating AICc for regression with ARMA errors

I am unable to manually calculate the AICc for a regression with ARMA errors. I would appreciate any help, such as: (1) pointing out what I am doing wrong or not doing; (2) advice on a textbook that ...
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Brief characterizations of AIC and BIC: how helpful are they?

I have found the following one-sentence characterizations of AIC and BIC in a lecture note: AIC estimates the degree to which the predictive accuracy of the model will generalize to new data. BIC ...
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Model Selection with AIC. Choosing between negative and positive AIC values

I have gone through the model selection process for my linear model prior and post model transformation. I would like to know whether it is ok for me to compare the AIC values for both models given ...
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AIC scores the same

I am creating a series of linear mixed models using lmer (package lme4 in R) followed with model selection using AIC. The models are interactive with 6 explanatory variables (5 factors, 1 continuous ...
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Selecting the best regression model using R2 and AIC - what is the best approach?

I have a dataset in which I have one dependent and 3 independent variables (y ~ x1 + x2 + x3). For exploratory analysis, I have fitted the following models (using R): ...
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Interpreting why a VAR produces lower error than VARMA?

I trained various VARMA models on the same dataset consisting of different number of AR and MA terms, from $VARMA(0,1)$ and $VARMA(1,0)$ to $VARMA(6,6)$ and all the combinations in-between. After ...
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Is it okay to use AIC values generated from summary() for glmer?

I am conducting a random effect logistic regression using glmer in R. I have 13 different predictor variables, which I am evaluating at 4 different spatial scales (i.e. I have 13 variables derived ...
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Does empirical cumulative distribution function (ECDF) has its Akaike information criterion (AIC)?

Working on multivariate distribution fitting, and right now I have marginal univariate transform models and a copula model. Was thinking if I pick ECDF for marginals, do I still have meaningful AIC? ...
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3 votes
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What makes deep learning model complexity so different that conventional measures are not sufficient?

Currently, there are some efforts to define a complexity measure for deep learning models. Such as topological, spectral ergodic, see in-depth recent survey. What makes deep learning model complexity ...
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ARIMA accuracy measures, rolling forecast

Regarding ARIMA model selection and especially accuracy measures several questions came into my mind. To shortly summarize, in my understanding, after necessary transformations/differencing, p and q ...
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Optimal lag length in VAR/VECM: IC or Residual test?

I read so many answers in here that I should use IC(information criteria) to determine the optimal lag length in VAR/VECM. But also it is important to check the residual of VAR/VECM has no-...
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computing model averaging of coefficients of select models using cencorreg function from NADA2 package

I am using the cencorreg function in the NADA2 package to examine the relationship between chemical concentrations in various types of tissues (4 tissues = protein, polar-lipids, non-polar lipids, and ...
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1 vote
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Compare linear regression slopes between non-nested models with differing dataset sizes

I'd like to test if the slopes of two linear regression models differ. However, the caveat is that one of the regressions fits a subset of the data, and the other fits the whole dataset. The two ...
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Which link function in binomial regression is better?

Concerning the choice of the link function in binomial regression (e.g. logit versus probit or cauchit), I wonder what the recommended comparison criterion might be. Note that I am not interested in ...
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Negative Infinity AIC and BIC

I was trying to compare best fit model for monthly precipitation data sets and negative and positive infinity (-inf and inf) as values have showed up for both AIC and BIC tests. Can anyone tell me ...
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Best-Subset Regression based on BIC versus Forward Selection based on AIC

I am trying to get a better grasp of BIC and AIC scores. I know BIC has a harsher penalty than AIC regarding model size (it prefers smaller, less complex models). Suppose there is a situation where I ...
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AIC comparison of GAMM and LMM: Is it valid?

I have 2 models, using exactly the same variables, one fitted as a linear mixed model (LMM) and another fitted as a generalised additive mixed model (GAMM). I am interested in the fixed effect X and ...
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How does the R function confint calculate confidence intervals for a model averaged object from MuMIn?

I've been using the MuMin package in R to create model averaged estimates from a candidate model set and the documentation suggests that you can use confint() to create confidence intervals for the ...
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How can I compute likelihoods using MATLAB for custom distributions?

I have some data and several models that I would like to compare using AIC and BIC. I need to compute likelihoods to use the information criteria; the problem is that the data are from a custom ...
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Is there a universal way to calculate model likelihoods for an arbitrary distribution?

I have no background in statistics but have been tasked to use AIC and BIC to select a model for some observed experimental data. The population data cannot be assumed to obey any particular ...
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