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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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Validity of AIC When Comparing Models with Varying Dispersion Parameters

I'm currently making a binomial model with a logit link, which is parameterised as a quasibinomial since I'm allowing it to calculate the dispersion parameter. I was wondering, since changes to the ...
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Comparing Models with Unequal Sample Sizes

I have performed an association analysis where I have associatiated several different perdictor variables to a dependent variable. For each predictor, I run two models and compare them via the ...
CAM_etal's user avatar
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comparing non-nested models with different specifications based on AIc/BIC criteria

I am trying to determine if I can use the AIC/BIC criteria for model selection in the case of a multivariate probit model. I have two models with different specifications: e.g. Model-1: mvprobit ( Y1 =...
Jay Shah's user avatar
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calculating QAIC for GLM

I am trying to calculate QAIC of DLNM models with code offered in here. but I am confused about the difference between this and the formula of QAIC from other R package as in the answer to this ...
user25650260's user avatar
4 votes
1 answer
86 views

Comparing Firth's logistic and traditional logistic by AIC

My data has rare events so I decided to develop a Firth's penalized logistic regression using logistf package. I also want to apply a traditional ML logistic ...
AmirMohammad's user avatar
1 vote
0 answers
22 views

Calculate weight for GLM-quasi poisson model

I am running several models with the quasi-Poisson family. I am looking at data from vulture restaurants. Vulture count was modelled at each site as a function of either a linear or quadratic effect ...
Emeline AUDA's user avatar
9 votes
0 answers
93 views

Any Insights on the adoption and use of the Healthy Akaike Information Criterion (hAIC)?

Recently, I came across the Healthy Akaike Information Criterion (hAIC), introduced by Demidenko in his 2004 book "Mixed Models: Theory and Applications with R." Despite its (potential) ...
Robert Long's user avatar
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3 votes
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Normality of residuals versus AIC and "best" fit

Hoping to get some insight into normality of residuals vs the "best" fit of the model. After running a simple linear regression and checking normality of the residuals, I logged my outcome ...
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GAMLSS - How to adjust AIC for log-transformation of variables [duplicate]

I have one GAMLSS model in which the independent and dependent variable are on the natural scale, and a second model in which both variables have been log-transformed. How do I adjust the AIC of the ...
Peder Holman's user avatar
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22 views

Is the behavior of log-likelihood and number of parameters correct in probabilistic PCA?

I am studying the behavior of Probabilistic PCA as described by Tipping and Bishop (1999). I am using the R package "Rdimtools" to help. I am puzzled about the number of parameters in the ...
Daniel Caetano's user avatar
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35 views

Different P-Value and AIC before/after standardization [Python - Statsmodels]

I am investigating the correlation between environmental variables (15 continuous variables grouped as 'DHIs' in the code below) and fox occurrence (binary), using logistic regression / Python ...
Andrew Norfield's user avatar
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1 answer
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How does lmList calculate AIC?

I'm using the lmList function in the lme4 R package to fit the following model: mod.list <- lmList(record ~ log(LL)*site | species, data = dat, family=binomial) ...
stweb's user avatar
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1 vote
0 answers
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Is comparing the AIC of a Bayesian and a frequentist model right?

I’m trying to fit a general linear model where the dependant variable is a probability. It is zero-inflated and continuous, then following the advice here blog of Ben Bolker, I separated my data pool ...
Auvray alexandre's user avatar
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1 answer
27 views

Additional covariate reduces AIC in mixed models (LMM, GLMM, GAM)

In repeated measure for timepoints in different Group, Age and Gender act as a covariate in ...
hey0god's user avatar
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1 vote
0 answers
19 views

Model Fit Measures in a Binomial Logistic Regression

I am very new to regression statistics and have produced four models in the statistical package Jamovi using binomial logistic regression. Looking at model fit measures I am confused as the results ...
Max's user avatar
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7 votes
3 answers
379 views

AIC model selection is keeping a variable with p = 0.47

I am modeling migration departure timing for swallows to try and figure out which of the predictor variables that I have data for influenced departure timing. All of the predictors are variables that ...
purplebubbles93's user avatar
1 vote
0 answers
24 views

AIC correction for unusual transformations of the response

When using transformations of the response variable, one should correct the AIC of the transformed model in order to be possible to compare it to the unstransformed model (see here). The example used ...
droubi's user avatar
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0 answers
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Mixing MLE and Hypothesis Testing

In my field of biology, we have spatial data with known distances, so we know a general formula for our error structure that we can use in a generalized least squares context, it is $$ (1-\lambda)\...
A Friendly Fish's user avatar
2 votes
2 answers
63 views

AIC interpretation: why is lower AIC better?

Why is AIC interpreted as lower is better, intuitively, can someone explain? I am running these two models, of which m1 has lower AIC when compared to ...
four77's user avatar
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0 answers
26 views

Handling Non-normality of Reaction Time Data in Mixed Models

I am examining the effect of 'Phase' on reactions time (RT) data using a mixed model in lme4. However, as is common with RT data, the residuals are non-normal. This is the first model, which is a ...
SilvaC's user avatar
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0 answers
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How to calculate AIC of a spatial panel model in R?

This may be a better question for Stack Overflow, but I think a statistics answer may be warranted here: I have built some spatial panel models in R using the ...
geoscience123's user avatar
3 votes
1 answer
56 views

What's the relationship between "bias-variance tradeoff" and "consistent model selection"?

I'm very confused about the relationship between "bias-variance tradeoff" and "consistent model selection". Based on my current interpretation, the ultimate goal of taking care of ...
ExcitedSnail's user avatar
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1 vote
1 answer
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How do I prioritise model diagnostics while considering model selection and parameter uncertainty?

I have fitted a generalized linear mixed model using glmmTMB on the data (110 observations, balanced data) collected from an observational study to understand the ...
medium-dimensional's user avatar
2 votes
1 answer
175 views

How to determine the best fitted model by AIC between lm(y~x),lm(log(y)~x), drc(y~x) in R

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user408308's user avatar
0 votes
1 answer
23 views

How many lags to insert into a GARCH(m,p) model?

My question might be trivial, but the doubt arises due to different ways of dealing with modeling that I have found in different research papers. In particular, I was able to observe that (in time ...
Giuseppe Vonella's user avatar
0 votes
0 answers
15 views

Comparing and selecting models, constructing objective function (complexity, prior knowledge on the distribution of parameter values)

We have a set of models that were derived using some fitting routine that optimizes parameter values utilizing $\chi^2$ for a given model. model1 has 100 parameters, model2 has 99 parameters, ... ...
twistfire's user avatar
  • 113
1 vote
1 answer
29 views

Interpreting AIC relative likelihoods ( qpcR::akaike.weights() )

I want to ensure that I am correctly interpreting AIC relative likelihood (RL) scores, specifically those returned by qpcR::akaike.weights$rel.LL. For example, I ...
PhelsumaFL's user avatar
2 votes
1 answer
28 views

Why Adjusted R^2 falls if I include both individual and time fixed effects?

I have a (probably simple) question on fixed effects estimation. I am trying to do baseline growth regressions of log GDP per capita against a number of covariates and, in line with the literature, I ...
last_resource's user avatar
0 votes
0 answers
14 views

Diebold-Mariano Revisited: what is a reasonable parameter count for information criteria when the model is complex?

Diebold (2015) wrote a follow-up paper/essay reflecting on how his work with Mariano to develop the Diebold-Mariano test has been abused over the years. One of the main points in the follow-up paper ...
Dave's user avatar
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0 votes
0 answers
32 views

Finding significant changepoints using Segneigh method

I am analyzing changes in the data set in time as mentioned below. To find significant changepoints in time, I am using the 'changepoint package', cptmeanvar function, SegNeigh method, and AIC as the ...
Aarushi Gupta's user avatar
4 votes
2 answers
255 views

Generalized additive model: Variable & model selection

I know this type of question has been asked many times before, so I apologize for re-posting about it. I bring it up again because it's been taught in one of my courses of study and I want to make ...
Nate's user avatar
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28 views

How should MacKenzie and Bailey goodness-of-fit test be used for several models at a time in model selection?

I am running single-season occupancy models and I usually have many selected "best models" by AICc criterion. At the time, I am running MacKenzie and Bailey's (2004) goodness-of-fit test for ...
Magdalena Salas's user avatar
1 vote
0 answers
31 views

AIC score and purpose of logLik to compare residual variance of models

I am comparing the residual variance of two regression models, one fitted with a simple linear regression model and the other fitted with a mixed-effects model using the lme4 package. I am using the ...
four77's user avatar
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4 votes
0 answers
27 views

Why do model selection criteria (xICs, etc) not explicitly incorporate a loss function?

Model Selection and Multimodel Inference by Burnham and Anderson notes that TIC, AIC, AICc and QAICc are based on K-L distance between a given model and true model. Also BIC is in a sense based on ...
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9 votes
2 answers
304 views

Is AIC or BIC preferred for prediction/explanation?

This answer (currently 89 upvotes) states: AIC is best for prediction as it is asymptotically equivalent to cross-validation. BIC is best for explanation as it is allows consistent estimation ...
Mohan's user avatar
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1 vote
1 answer
72 views

Calculate (quasi) AIC for mixed-effect baseline-category (multinomial logit) model

I am doing a discrete choice experiment where respondents are presented with different patient profiles, and for each profile, respondents need to choose one (out of three) treatment options. An ...
Trang Hien's user avatar
0 votes
0 answers
34 views

AIC calculation

Our object is calculating AIC, and we are unsure whether we can use our measure below when calculating AIC. The following are the data from the experiments and our method to calculate the information ...
user avatar
3 votes
1 answer
98 views

Interpreting AIC Values, how do I know what is significant?

I have run 16 parsimonious models to understand how a combination of different land management practices affect different soil health measures. As a result, I have a spreadsheet of AIC values, some of ...
EBH's user avatar
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0 votes
0 answers
37 views

BMA formula with BIC

I am interested in using Bayesian modele averaging as a selection creteria (BMA) vs AIC. I read that BMA is widely implemented in clustering models. Suppose that we need to fit M models to a data and ...
Alice's user avatar
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0 votes
0 answers
35 views

AIC-BIC in mixed models

I was reading about mixed models and I am confused about AIC and BIC criteria. My first question is can I use this types to calculate them? AIC=2d-2ln(l) BIC=dln(n)-2ln(l) where d: is the numbers of ...
Superd's user avatar
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2 votes
0 answers
26 views

Reporting AIC values for model fits with multiple runs - report the min or the average?

I have a model that I fit with NLL minimization. I fit that model n times with random starting values, to try to avoid local minima. When reporting the results, it seems reasonable to me to report the ...
Danny Garside's user avatar
0 votes
0 answers
72 views

Model comparsion for robust linear mixed models (robustlmm)

I'm currently working on a project where I've fitted 4 robust linear mixed models. However, I've hit a bit of a roadblock when it comes to model selection. I've been using the AIC (Akaike Information ...
Igor Bione's user avatar
1 vote
0 answers
82 views

Using step() and car::vif(): order matters?

When fitting linear models and coming up with a plausible one, AIC and VIF are often used. However, I notice that the order in which the methods are used makes a difference on the final model. Should ...
compbiostats's user avatar
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1 vote
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Is it possible average an information criterion across models?

Is it possible to take the average of information criterion like the AIC? For my model comparison, I have 24 different models. I use 4 different GARCH models each with 6 different distributions for ...
Hello there's user avatar
0 votes
0 answers
115 views

Use linear mixed model or linear quantile mixed model for non-normal residuals?

I started with this initial model: m1 <- lmer(response ~ treatment + (1|subjectID), data = data) However, the residuals of the model are heavy-tailed (presumably enough to violate the normality ...
Jade's user avatar
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6 votes
1 answer
384 views

Does information criteria (AIC, BIC and DIC...) imply "causality"?

I am interested in finding out the graphical causal structure. Causal Discovery algorithms (e.g., DAG learning) are used to identify potential causal graphs. In score-based causal discovery methods, ...
Jay's user avatar
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1 vote
0 answers
114 views

Residuals and AIC

AIC is normally calculated using the maximum likelihood, so you must have some probability distribution to work with. But I saw some formulas using the sum of squared residuals or the mean absolute ...
FaresDjerourou's user avatar
1 vote
0 answers
159 views

Which evaluation metric should I choose? AIC or MSE?

I am currently at a total loss, so I hope someone can point me in the right direction regarding my model selection. The situation I want to create a linear model that best forecasts my data. I am ...
eork's user avatar
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0 votes
0 answers
33 views

Rejection of ADF-test for log returns and AIC selected ARIMA(0,0,0) and ARIMA (0,0,0) with a drift?

I use monthly log returns for some stock portfolios and rejects the null of the ADF-test for both. Hereafter I use AIC to select best fitting models using auto.arima in R. The selected models are ...
NotJohnLeCarre's user avatar
0 votes
0 answers
88 views

AIC and BIC Manual Calculations Are a Bit Off From Statsmodels Estimates in Python

I ran a multiple regression using statsmodels. I wanted to verify my understanding of calculations for log-likelihood (ll), AIC and BIC. So I attempted to manually calculate the ll, AIC and BIC for ...
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