Skip to main content
Cross Validated

Return to Question

Notice removed Authoritative reference needed by CommunityBot
Bounty Ended with no winning answer by CommunityBot
added 168 characters in body
Source Link
Andrew Mao
Andrew Mao
  • 261
  • 1
  • 7

Let's say I have a model selection problem and I am trying to use AIC or BIC to evaluate the models. This is straightforward for models that have some number $k$ of real-valued parameters.

However, what if one of our models (for example, the Mallows modelMallows model) has a permutation, plus some real-valued parameters instead of just real-valued parameters? I can still maximize the likelihood over the model parameters, for example obtaining a permutation $\pi$ and a parameter $p$. However, how many parameters does $\pi$ count toward for computing AIC/BIC?

Let's say I have a model selection problem and I am trying to use AIC or BIC to evaluate the models. This is straightforward for models that have some number $k$ of real-valued parameters.

However, what if one of our models (for example, the Mallows model) has a permutation, plus some real-valued parameters instead of just real-valued parameters? I can still maximize the likelihood over the model parameters, for example obtaining a permutation $\pi$ and a parameter $p$. However, how many parameters does $\pi$ count toward for computing AIC/BIC?

Let's say I have a model selection problem and I am trying to use AIC or BIC to evaluate the models. This is straightforward for models that have some number $k$ of real-valued parameters.

However, what if one of our models (for example, the Mallows model) has a permutation, plus some real-valued parameters instead of just real-valued parameters? I can still maximize the likelihood over the model parameters, for example obtaining a permutation $\pi$ and a parameter $p$. However, how many parameters does $\pi$ count toward for computing AIC/BIC?

Notice added Authoritative reference needed by EngrStudent
Bounty Started worth 50 reputation by EngrStudent
Tweeted twitter.com/#!/StackStats/status/352879858872889345
Source Link
Andrew Mao
Andrew Mao
  • 261
  • 1
  • 7

AIC/BIC: how many parameters does a permutation count for?

Let's say I have a model selection problem and I am trying to use AIC or BIC to evaluate the models. This is straightforward for models that have some number $k$ of real-valued parameters.

However, what if one of our models (for example, the Mallows model) has a permutation, plus some real-valued parameters instead of just real-valued parameters? I can still maximize the likelihood over the model parameters, for example obtaining a permutation $\pi$ and a parameter $p$. However, how many parameters does $\pi$ count toward for computing AIC/BIC?