Is it possible that AIC = BIC?AIC & BIC number interpretationAIC, BIC, DIC, model selection...

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Is it possible that AIC = BIC?


AIC & BIC number interpretationAIC, BIC, DIC, model selection criteriaAIC,BIC,CIC,DIC,EIC,FIC,GIC,HIC,IIC — Can I use them interchangeably?AIC, BIC and GCV: what is best for making decision in penalized regression methods?How do you derive AIC and BIC for discrete-valued observables?Combining AIC and BICOverview of selection method for p-order of AR($p$) modelAre there circumstances in which BIC is useful and AIC is not?Use BIC or AIC as approximation for Bayesian Model AveragingVAR lag selection tests: Which one do I choose?













6












$begingroup$


Two well-known (and related) measures of model complexity from statistics are the Akaike Information Criterion (AIC) and the Bayesian Information
Criterion (BIC).



When might AIC = BIC?










share|cite|improve this question











$endgroup$








  • 10




    $begingroup$
    You should try writing down the formulas and setting them equal to each other :) You will get the answer immediately.
    $endgroup$
    – guy
    Mar 14 at 13:18
















6












$begingroup$


Two well-known (and related) measures of model complexity from statistics are the Akaike Information Criterion (AIC) and the Bayesian Information
Criterion (BIC).



When might AIC = BIC?










share|cite|improve this question











$endgroup$








  • 10




    $begingroup$
    You should try writing down the formulas and setting them equal to each other :) You will get the answer immediately.
    $endgroup$
    – guy
    Mar 14 at 13:18














6












6








6





$begingroup$


Two well-known (and related) measures of model complexity from statistics are the Akaike Information Criterion (AIC) and the Bayesian Information
Criterion (BIC).



When might AIC = BIC?










share|cite|improve this question











$endgroup$




Two well-known (and related) measures of model complexity from statistics are the Akaike Information Criterion (AIC) and the Bayesian Information
Criterion (BIC).



When might AIC = BIC?







aic bic






share|cite|improve this question















share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Mar 14 at 14:06









Richard Hardy

27.8k641128




27.8k641128










asked Mar 14 at 13:09









JanJan

1514




1514








  • 10




    $begingroup$
    You should try writing down the formulas and setting them equal to each other :) You will get the answer immediately.
    $endgroup$
    – guy
    Mar 14 at 13:18














  • 10




    $begingroup$
    You should try writing down the formulas and setting them equal to each other :) You will get the answer immediately.
    $endgroup$
    – guy
    Mar 14 at 13:18








10




10




$begingroup$
You should try writing down the formulas and setting them equal to each other :) You will get the answer immediately.
$endgroup$
– guy
Mar 14 at 13:18




$begingroup$
You should try writing down the formulas and setting them equal to each other :) You will get the answer immediately.
$endgroup$
– guy
Mar 14 at 13:18










1 Answer
1






active

oldest

votes


















17












$begingroup$

As a reminder:



$$AIC = - 2 log mathcal{L}(hat{theta}|X)+2k $$



$$BIC = - 2 log mathcal{L}(hat{theta}|X)+k ln(n)$$



So for what values of $n$ is $2 = ln(n)$?






share|cite|improve this answer









$endgroup$









  • 8




    $begingroup$
    (+1) I noticed that for $BIC$ you write $log$ and $ln$ in the same expression. Is this distinction necessary?
    $endgroup$
    – Sycorax
    Mar 14 at 19:23












  • $begingroup$
    Both logarithms have $e$ as their basis. It is just that log-likelihood (instead of ln-likelihood) is the term we use to describe the natural logarithm of the likelihood.
    $endgroup$
    – Stats
    Mar 14 at 19:27












  • $begingroup$
    @Sycorax: I guess it's to signify that for the $log$ it really doesn't matter (as long as you're consistent) whereas for the $ln$, well it has to be $e$.
    $endgroup$
    – Mehrdad
    Mar 15 at 0:37








  • 1




    $begingroup$
    But $n$ should be an integer, right?
    $endgroup$
    – innisfree
    Mar 15 at 5:44










  • $begingroup$
    @innisfree Not always. E.g., for generalized additive models the estimated degrees of freedom are usually not integers.
    $endgroup$
    – Roland
    Mar 15 at 7:12













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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









17












$begingroup$

As a reminder:



$$AIC = - 2 log mathcal{L}(hat{theta}|X)+2k $$



$$BIC = - 2 log mathcal{L}(hat{theta}|X)+k ln(n)$$



So for what values of $n$ is $2 = ln(n)$?






share|cite|improve this answer









$endgroup$









  • 8




    $begingroup$
    (+1) I noticed that for $BIC$ you write $log$ and $ln$ in the same expression. Is this distinction necessary?
    $endgroup$
    – Sycorax
    Mar 14 at 19:23












  • $begingroup$
    Both logarithms have $e$ as their basis. It is just that log-likelihood (instead of ln-likelihood) is the term we use to describe the natural logarithm of the likelihood.
    $endgroup$
    – Stats
    Mar 14 at 19:27












  • $begingroup$
    @Sycorax: I guess it's to signify that for the $log$ it really doesn't matter (as long as you're consistent) whereas for the $ln$, well it has to be $e$.
    $endgroup$
    – Mehrdad
    Mar 15 at 0:37








  • 1




    $begingroup$
    But $n$ should be an integer, right?
    $endgroup$
    – innisfree
    Mar 15 at 5:44










  • $begingroup$
    @innisfree Not always. E.g., for generalized additive models the estimated degrees of freedom are usually not integers.
    $endgroup$
    – Roland
    Mar 15 at 7:12


















17












$begingroup$

As a reminder:



$$AIC = - 2 log mathcal{L}(hat{theta}|X)+2k $$



$$BIC = - 2 log mathcal{L}(hat{theta}|X)+k ln(n)$$



So for what values of $n$ is $2 = ln(n)$?






share|cite|improve this answer









$endgroup$









  • 8




    $begingroup$
    (+1) I noticed that for $BIC$ you write $log$ and $ln$ in the same expression. Is this distinction necessary?
    $endgroup$
    – Sycorax
    Mar 14 at 19:23












  • $begingroup$
    Both logarithms have $e$ as their basis. It is just that log-likelihood (instead of ln-likelihood) is the term we use to describe the natural logarithm of the likelihood.
    $endgroup$
    – Stats
    Mar 14 at 19:27












  • $begingroup$
    @Sycorax: I guess it's to signify that for the $log$ it really doesn't matter (as long as you're consistent) whereas for the $ln$, well it has to be $e$.
    $endgroup$
    – Mehrdad
    Mar 15 at 0:37








  • 1




    $begingroup$
    But $n$ should be an integer, right?
    $endgroup$
    – innisfree
    Mar 15 at 5:44










  • $begingroup$
    @innisfree Not always. E.g., for generalized additive models the estimated degrees of freedom are usually not integers.
    $endgroup$
    – Roland
    Mar 15 at 7:12
















17












17








17





$begingroup$

As a reminder:



$$AIC = - 2 log mathcal{L}(hat{theta}|X)+2k $$



$$BIC = - 2 log mathcal{L}(hat{theta}|X)+k ln(n)$$



So for what values of $n$ is $2 = ln(n)$?






share|cite|improve this answer









$endgroup$



As a reminder:



$$AIC = - 2 log mathcal{L}(hat{theta}|X)+2k $$



$$BIC = - 2 log mathcal{L}(hat{theta}|X)+k ln(n)$$



So for what values of $n$ is $2 = ln(n)$?







share|cite|improve this answer












share|cite|improve this answer



share|cite|improve this answer










answered Mar 14 at 13:54









StatsStats

668210




668210








  • 8




    $begingroup$
    (+1) I noticed that for $BIC$ you write $log$ and $ln$ in the same expression. Is this distinction necessary?
    $endgroup$
    – Sycorax
    Mar 14 at 19:23












  • $begingroup$
    Both logarithms have $e$ as their basis. It is just that log-likelihood (instead of ln-likelihood) is the term we use to describe the natural logarithm of the likelihood.
    $endgroup$
    – Stats
    Mar 14 at 19:27












  • $begingroup$
    @Sycorax: I guess it's to signify that for the $log$ it really doesn't matter (as long as you're consistent) whereas for the $ln$, well it has to be $e$.
    $endgroup$
    – Mehrdad
    Mar 15 at 0:37








  • 1




    $begingroup$
    But $n$ should be an integer, right?
    $endgroup$
    – innisfree
    Mar 15 at 5:44










  • $begingroup$
    @innisfree Not always. E.g., for generalized additive models the estimated degrees of freedom are usually not integers.
    $endgroup$
    – Roland
    Mar 15 at 7:12
















  • 8




    $begingroup$
    (+1) I noticed that for $BIC$ you write $log$ and $ln$ in the same expression. Is this distinction necessary?
    $endgroup$
    – Sycorax
    Mar 14 at 19:23












  • $begingroup$
    Both logarithms have $e$ as their basis. It is just that log-likelihood (instead of ln-likelihood) is the term we use to describe the natural logarithm of the likelihood.
    $endgroup$
    – Stats
    Mar 14 at 19:27












  • $begingroup$
    @Sycorax: I guess it's to signify that for the $log$ it really doesn't matter (as long as you're consistent) whereas for the $ln$, well it has to be $e$.
    $endgroup$
    – Mehrdad
    Mar 15 at 0:37








  • 1




    $begingroup$
    But $n$ should be an integer, right?
    $endgroup$
    – innisfree
    Mar 15 at 5:44










  • $begingroup$
    @innisfree Not always. E.g., for generalized additive models the estimated degrees of freedom are usually not integers.
    $endgroup$
    – Roland
    Mar 15 at 7:12










8




8




$begingroup$
(+1) I noticed that for $BIC$ you write $log$ and $ln$ in the same expression. Is this distinction necessary?
$endgroup$
– Sycorax
Mar 14 at 19:23






$begingroup$
(+1) I noticed that for $BIC$ you write $log$ and $ln$ in the same expression. Is this distinction necessary?
$endgroup$
– Sycorax
Mar 14 at 19:23














$begingroup$
Both logarithms have $e$ as their basis. It is just that log-likelihood (instead of ln-likelihood) is the term we use to describe the natural logarithm of the likelihood.
$endgroup$
– Stats
Mar 14 at 19:27






$begingroup$
Both logarithms have $e$ as their basis. It is just that log-likelihood (instead of ln-likelihood) is the term we use to describe the natural logarithm of the likelihood.
$endgroup$
– Stats
Mar 14 at 19:27














$begingroup$
@Sycorax: I guess it's to signify that for the $log$ it really doesn't matter (as long as you're consistent) whereas for the $ln$, well it has to be $e$.
$endgroup$
– Mehrdad
Mar 15 at 0:37






$begingroup$
@Sycorax: I guess it's to signify that for the $log$ it really doesn't matter (as long as you're consistent) whereas for the $ln$, well it has to be $e$.
$endgroup$
– Mehrdad
Mar 15 at 0:37






1




1




$begingroup$
But $n$ should be an integer, right?
$endgroup$
– innisfree
Mar 15 at 5:44




$begingroup$
But $n$ should be an integer, right?
$endgroup$
– innisfree
Mar 15 at 5:44












$begingroup$
@innisfree Not always. E.g., for generalized additive models the estimated degrees of freedom are usually not integers.
$endgroup$
– Roland
Mar 15 at 7:12






$begingroup$
@innisfree Not always. E.g., for generalized additive models the estimated degrees of freedom are usually not integers.
$endgroup$
– Roland
Mar 15 at 7:12




















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