Questions tagged [model-averaging]
The process of combining different models to get a better resulting model than any of the constituents. Eg, computing a parameter estimator as the average of the estimators from each component model.
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GLMM Model Averaging with Predictor Multicollinearity
I am running GLMM models to determine how environmental factors influence bird collisions. I've obtained a list of candidate models with delta AIC less than 2, and I want to perform model averaging.
I ...
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How to calculate $P(f_1(X) = \text{max}(f_1(X), \dots, f_K(X))$ when $X$ is multivariate Normal?
Let's say I have a multivariate distribution $\mathbf{X} \sim \text{MVN}(\mathbf{\mu}, \mathbf{\Sigma})$ and a set of $K$ scalar functions of $\mathbf{X}$, $f_1(\mathbf{X}), \dots, f_K(\mathbf{X})$. ...
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Computing Bayesian model averaged posteriors
The Bayesian model averaged posterior predictive distribution for new data $\tilde{y}$ given training data $y$, across a set of $M$ models $\mathcal{D} = \{D_{1}, ..., D_{M}\}$, is defined as:
\begin{...
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Interpreting Contradictory Results in Bayesian Model Averaging: High Posterior Inclusion Probability with Unclear Effect
In my research, I am utilizing the Bayesian Model Averaging (BMA) methodology to identify the best set of regressors that can predict the outcome variable $y$. My dataset consists of five variables ...
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model averaging with MARSS
I have a large number of MARSS dynamic factor analysis models that, based on AICc, are all competitive for the 'best' model. Is there a way to implement a model averaging process so that I can extract ...
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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 ...
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Cross validation with GLMMs; best way to partition train and test data with regard to random effects?
I have some analysis I'm working on and I'm having a hard time nailing down the correct approach to take. I am modeling the dynamics of frog choruses by looking at what predicts the outcome of calling ...
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Help with Bayesian model averaging
I am new to statistical modelling and I need some help with Bayesian model averaging.
I have 3 models and I would like to derive a BMA of these models.
I am using the BIC estimate for the different ...
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General question on model weighting and averaging
I've had a stats related question I've been wondering for a while, but for which I have yet to find good sources on. I'm aware of things like the Akaike Information Criterion for weighting candidate ...
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Weird AIC weights in model averaging
I conducted AIC selection on a set of models, and isolated my top 3 models. I then calculated the AIC weights of each model, and got the values 0.99, 1.92e-14,and 6.9e-18. I never saw weight values ...
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Is it possible to average models with different link functions?
While I can compare models that have different link functions in terms of AIC/BIC weights, I think it's impossible to use those weights to create an averaged model. Am I right in believing this?
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Bayesian model averaging
In which situation should one refrain from using BMA? It seems to me that it is always a good idea to use the posterior probabilities when inferring/predicting.
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When/what to standardize for model-averaging with(out) interactions
I’m using the {MuMIn} package in R to select models (dredge, get top models, average etc). My question is about whether I need to, or should, standardise my ...
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Averaging SVM and GLM results: sensible or stupid?
I have taken two different approaches to calculate probability: using a GLM and an SVM. They are giving slightly different results (which is understandable, they are completely different approaches). ...
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BMA and RJMCMC predictive performance
We have a family of statistical models, with parameter spaces of different dimensions, which we aggregated through standard Bayesian Model Averaging (BMA). Experiments using training and test sets ...
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