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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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Is this Bayesian model averaging?

A classical example of Bayesian model averaging (BMA) is the regression setup where the choice of different sets of covariates corresponds to different models $\mathcal{M}_k$, $k = 1, \ldots, K$, ...
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63 views

Can model averaging be applied on models fitted to different data sets?

I have two data sets collected from two different sets of participants on their behaviour. EDIT: Both have the same response variables (Propensity to behave in a certain way - Yes or No). But they ...
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1answer
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BMA - at what posterior effect probability can we say that a variable has an effect?

in the setting of a Bayesian model averaging, we are not dealing with P--value when we are assesing variable importance, but with the posterior effect probability of each variable. My question is, at ...
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Estimate expected power on a wind turbine based on other nearby wind turbines

I'm looking for a reliable way to estimate the power that a wind turbine should be producing, based on the power that its neighbours are producing. We use this to identify turbines that are ...
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13 views

Robustness checks vs. BMA

I have a simple question. Let's say I have a model with 13 IVs and I am using a BMA for analysis. At the end, I would like to add few other variables for robustness checks. However, isn't BMA by ...
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37 views

R: Interpreting bayesian model averaging of multinomial logit

I am having troubles interpreting results from a BMA of a multinomial logit. THE SETUP My goal is to analyze how companies choose a payment method in M&A based on the acquirer's financial ...
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1answer
50 views

Interpretation of a classic multinomial logit vs. BMA of multinomial logit

EDIT #1 Most likely I have set up the function bic.mlogit in a wrong way. @Jesper Hybel, hopefully, directed me in the right way. With the new setup I get two sets ...
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1answer
38 views

vector of weights

I am using the MuMIn package for model averaging. However, I am not clear of the function par.avg(). In this function, we need to specify the following, ...
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1answer
90 views

Effect sizes for model averaging

The goal is to find if one factor is stronger than the other in the models I have considered. I am using the information-theoretic approach. Since $n/K>40$, I am using AIC. Firstly the model is ...
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1answer
274 views

For model-averaging a GLM, do we average the predictions on the link or response scale?

To compute the model-averaged predictions on the response scale of a GLM, which is "correct" and why? Compute the model averaged prediction on the link scale and then back-transform to the response ...
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44 views

Model Averaging errors using lme4 in R

I am running a glmer model on a response variable with binomial distribution and random term. My data has 3 explanatory categorical variables and I have successfully run dredge() on them and their ...
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391 views

Model averaging in mixed models

I am reading this blog post regarding using model averaging for mixed models and the last para says "https://sites.google.com/site/rforfishandwildlifegrads/home/mumin_usage_examples" ...
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1answer
133 views

Model-averaged for a glm.nb [closed]

How can i do a model averaged for a negative binomial model. I try with the AICcmodavg but it is not compatible with glm.nb models. Any help would be appreciated Thank you very much Magdalena
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23 views

New Model from average of n-many models

Say I read three studies that predict reaction time based on participants age. I might collect three regression equations with an intercept a slope for age and an error term. After reading three ...
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1answer
155 views

How does model averaging with a categorical variable work?

I have a series of models (~14) which do not include a categorical variable. One model (#15) however, does have a categorical variable with 3 levels. Normally for this model one of the categories ...
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234 views

McFadden Pseudo R² in averaged model

McFadden's pseudo-R² is a well-known coefficient of determination. If I am right, it can be calculated by 1-(mod_deviance/null_deviance), where ...
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1answer
168 views

Model averaging and intercept interpretation in Splines

I have two questions regarding Multivariate Adaptive Regression splines (MARS) 1) How is intercept interpreted in MARS? In linear regression, my intercept is shown in the image below In a linear ...
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1answer
404 views

Combining bagging and stacking, with and without clusters and heteroskedasticity

Question 1: Start with the classing case of bagging, say in random forest. Fit $B$ trees to bootstrap samples of the data. Average the predictions of the $B$ trees to form a final prediction. ...
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Can you increase confidence from two similar predictions based on independent data

Let's say I have two models to predict the out come of a game. Model A uses the player specific statistics (speed, reaction times etc.) and predicts a 75% chance of a win for my team. Model B uses ...
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234 views

Confidence interval of averaged-regression fit

I have a model; Y ~ mX, where Y is a set of new random number generated using Poisson distribution. i.e $Y_i \sim \text{poisson}(H_i)$. Hence I am performing 100 replications, during each replication,...
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1answer
23 views

Is the average of multiple function's minimum equivalent to the minimum of the average function of the multiple functions?

Sorry if the wording is confusing, I feel it accurately articulates the question although being a bit of a head teaser. Essentially I am running many cross-validated models. Each model produces a (...
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3answers
149 views

Models combinations

My goal is time series forecasting. I have created a number of models to make predictions. I know that forecast quality can be improved by combining predictions from different models(linear ...
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825 views

One multiple linear regression, or many simple linear regressions for prediction?

I would like to create a model which predicts the amount of energy used in an area, dependent on the number of properties in 5 categories (detached, semi, flats, bungalow and terrace). I have daily ...
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1answer
53 views

Variable Selection and Model Likelihood

Can I derive any (Bayesian) statements about the probability of a parameter given the data and the marginal likelihood of models containing this parameter? For instance, consider a regression setup $$...
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73 views

Ensemble, what are simple alternatives to averaging?

I have a classification problem (A or B or C). I am currently evaluating test set results from the trained random forest, neural net, and logistic regression models. Any one model works pretty well ...
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89 views

Panal data, simple model averaging in R

For my master thesis I working on model averaging for panel data. So far I was not able to use common model averaging packages (BMS, BMA) because I use panel data (plm regression first differences). ...
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3answers
93 views

Arguments against model or forecast combination?

Do you know any references providing arguments against model or forecast (models output) combination? Could not find anything
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1answer
282 views

Do model averaging and model combination mean the same?

I am not sure, but I guess model averaging and model combination and even forecast averaging and forecast combination are used arbitrarily in the literature... Is this only my feeling or indeed the ...
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1answer
1k views

Use BIC or AIC as approximation for Bayesian Model Averaging [duplicate]

I want to compare "real" Bayesian Model Averaging (BMA) performed with the EM algorithm and information-criterion based BMA. Which one, BIC or AIC, is a "closer" approximation to the "real" BMA? BIC ...
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1answer
818 views

Are these approaches Bayesian, Frequentist or both?

currently I am comparing different combination methods: Equally Weight Averaging -> deterministic Ordinary Least Squares Averaging -> frequentist? Bayesian Information Criterion Averaging -> both, ...
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1answer
58 views

Bayesian model averaging for beta estimation

I am trying to apply BMA to estimate the beta of a certain stock by combining different models. One, simple model to estimate beta is this $$\beta_{i,T} = \frac{{\rm cov}(r_{i,t},r_{M,t})}{{\rm var}(...
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2answers
188 views

Bayesian Model Averaging: How to use in this example?

Let ${M_1, M_2}$ denote two competing forecasting models. With Bayesian model averaging we can get $p(y_{T+h}|y_{1:T}) = \sum_{j=1}^2p(y_{T+h}|y_{1:T},M_j)*p(M_j|y_{1:T})$ $1:T$ represents the ...
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1answer
931 views

Model averaging approach — averaging coefficient estimates vs. model predictions?

I have a basic question regarding approaches to model averaging using IT criteria to weight models within a candidate set. Most sources that I have read on model averaging advocate averaging the ...
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239 views

Bayesian Model Averaging with MCMC draws

I aim at improving my understanding of Bayesian model averaging in the context of predictions and I am somewhat stuck on how to implement this numerically. Lets say I have two models $\mathcal{M}_1$ ...
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1answer
244 views

Weights to combine different models

I have built different classification models (logistic regression, randomforest, and xgboost) for a dataset. I would like to combine the prediction of all the models to reduce the variance and ...
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1answer
216 views

What exactly is the Bayesian model average probability of the following?

I am struggling to understand Bayesian Model Averaging. Suppose I have got a simple Bayesian network, $X\to Y$ where X and Y are binary variables and I have got 2 models: Model 1 (simple): $$P(Y=1\...
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2answers
85 views

Model averaging

We have the equation: $P(C|X)=\sum_{i}P(C|X,M_{i})P(M_{i}|X)$ I should technically be able to prove that it works even when the total number of models is 1, so I go from the right hand side of the ...
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110 views

Average multiple model predictions and standard errors

Suppose I am fitting a Generalized mixed effects model (R lme4 package ) for a number of permutations (randomized subsets of the ...
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1answer
330 views

Calculating QAICc and Averaging GLMM models with various overdispersion

I am having difficulty figuring out how to calculate a dispersion parameter to calculate QAICc for a GLMM with a binomial fit. I have tested for overdispersion using this code: ...
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728 views

How to implement Bayesian Model Combination?

I'm interested in formal procedure mentioned in "Turning Bayesian Model Averaging Into Bayesian Model Combination" (Kristine Monteith 2011). I have a set of $N$ "best" AIC ranked models and I want to ...
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2answers
8k views

Interpreting model averaging results in R

I am trying to understand and know what to report from my analysis of some data using model averaging in R. I am using the following script to analyse the effect of method of measurement over a given ...
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415 views

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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1answer
752 views

Confidence bands for model averaged predictions of GLMMs

I use R with the MuMIn package for Multimodel inference. My global Model is ...
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0answers
54 views

What would be a proper statistical test for multiple models and model selection?

Suppose I have a data set of N observations (n = 1...N) for out-of-sample estimation and values of ($y_n$). I have also ...
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1answer
239 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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362 views

State of the art for bagging/model averaging? [closed]

If I estimate a collection of models predicting $Y$ by $\hat{Y}$, which methods are out there to combine these forecasts? Which methods work well/best (and why?) to improve prediction accuracy? My ...
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1answer
1k views

GLMER sampling random effects

I have a model M calculated via lme4's glmer function, with random effects ("Customer ID") and fixed effects for each customer ID. My dataset is very large, so I would like to select a sample of ...
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2answers
3k views

Is this the state of art regression methodology?

I've been following Kaggle competitions for a long time and I come to realize that many winning strategies involve using at least one of the "big threes": bagging, boosting and stacking. For ...
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2answers
1k views

How do you determine the weight of different sample sizes?

I'm not intimately familiar with statistics, please keep your answers in the realm a layman could understand. I've been tasked to create rankings for QA scores at a company. I could just use the ...
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1answer
288 views

learning shared weights

I have a classification problem with 100 variables measured during 5 conditions, so 500 features. Some conditions are more informative than others, so I would like to have a learner that learns ...