What Does arg Mean in Maximum Likelihood Estimation?

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In maximum likelihood estimation, "arg" refers to the argument that maximizes the likelihood function, indicating the specific parameter value that achieves this maximum. The expression \(\widehat{\theta} = \underset{\theta}{\operatorname{arg\ max}}\ \mathcal{L}(\theta)\) highlights that \(\widehat{\theta}\) is the value of \(\theta\) for which the likelihood function \(\mathcal{L}(\theta)\) is maximized. While one could express the maximum likelihood without "arg," using it clarifies that the focus is on the parameter value itself rather than just the maximum likelihood value. This distinction is important for understanding the estimation process in statistical modeling. The discussion emphasizes the necessity of "arg" for clarity in defining the maximization context.
Simfish
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http://en.wikipedia.org/wiki/Maximum_likelihood


What exactly does the "arg" here mean? It seems to be an unnecessary - the max L(\theta) seems to be sufficient enough. Or am I missing something?
\widehat{\theta} = \underset{\theta}{\operatorname{arg\ max}}\ \mathcal{L}(\theta).
 
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arg is needed because the left-hand side is the argument of L.

One can write L(\widehat{\theta}) = \underset{\theta}\max\ {L(\theta)}..

Or one can write \widehat{\theta} = \underset{\theta}{\operatorname{arg\ max}}\ {L}(\theta).
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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