All Questions
Tagged with model-selection bic
107
questions
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) ...
- 64.1k
1
vote
1
answer
44
views
BIC with non-negligible priors
I want to do model selection based on the best-fit/MAP/marginal posterior I find from an MCMC and likelihood maximization. I have a likelihood $\mathcal{L}(X|\theta)$, some informative priors $\pi(\...
- 13
3
votes
1
answer
55
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 ...
- 2,966
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 ...
- 939
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 ...
- 650
1
vote
0
answers
16
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Does anyone know how to model selection for function on function linear model to find the best subset of functional covariate in R [closed]
Does anyone know how to model selection for function on function linear model to find the best subset of functional covariate in R
- 11
1
vote
1
answer
176
views
BIC drop in regsubset summary different from manual calculation in R
The leaps library regsubset function gives an object that contains the list of BIC drops of each subset model from the intercept model.
However, it is different from what is calculated manually.
For ...
- 37
0
votes
0
answers
74
views
Exact computation of Bayes factor for multivariate normal
Question: Is there a known, exact expression for the Bayes factor between two multivariate normal hypotheses?
Let $H_1$ and $H_2$ be two subsets of $R^d$ with normal priors $\pi(\mu|H_j)$. The sets $...
- 1
1
vote
1
answer
459
views
How to report on model selection
I have run model selection and have been advised to use the BIC value to determine the best model for my purposes.
However, having looked around online I cannot seem to find a standard way to report ...
3
votes
1
answer
740
views
Why is BIC considered consistent (though AIC is mostly used) for large number of observation?
AIC, BIC are the famous criteria for model selection. But many times they show different results.
I read in several places that BIC is consistent while AIC is not. And AIC can achieve minimax rate but ...
user369390
1
vote
2
answers
739
views
How does the Bayesian Information Criterion work for model selection? [closed]
I am aware that we can use the BIC values from different models in order to determine which model predicts the data best. However, I'm a little confused about the criteria used to determine which ...
- 113
0
votes
0
answers
171
views
AIC and SC tend to select the maximum lag for VECM
I am building a VAR modell in order to discover how oil price shocks and 3 macroeconomic control variables (gdp_growth, Interest rate, exchange rate) influence core and headline inflation in the USA. ...
0
votes
0
answers
20
views
why in BIC Bayesian information criterion we ignore items independent of N [duplicate]
I wonder why BIC is defined in a way where items independent of N are dropped. In other words, why in the following image those in equation 6 that don't vary as N grows are ignored in equation 7. Is ...
1
vote
1
answer
207
views
BIC for generalized additive models
Is there any way to use BIC in model selection for gam? And if so then how to extract bic?
user369390
1
vote
2
answers
235
views
Normalising likelihood for BIC/AIC calculation
I am running some model inference using AIC and BIC. My problem is that when I go and calculate the (maximum) loglikelihoods of my models, they are usually really high (range between 4700 and 1400 ...
- 113