Proof of Hahn decomposition theorem

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The discussion focuses on understanding a specific inequality in the proof of the Hahn decomposition theorem, which pertains to signed measures. The theorem states that any measurable set can be expressed as a disjoint union of positive and negative sets. The proof involves showing that if a set A is not positive, one can find a subset E_0 with negative size and remove disjoint pieces E_k of positive size. The key argument demonstrates that if a measurable set F has a size exceeding a certain threshold, it contradicts the definition of m_k. The clarification provided helped resolve the confusion regarding the inequality's validity.
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I'm trying to read this proof, and I'm stuck on the inequality on page 27 following the statement "It follows that every measurable subset..." Why does it hold?

The theorem is about signed measures, i.e. functions that are like measures, but can assign both positive and negative "sizes" to sets. A measurable set is said to be positive if all its measurable subsets have a non-negative size. The term negative is defined similarly. The theorem asserts that the set X is a disjoint union of a positive set A and a negative set B. The strategy of the proof is roughly this: First find a set B and show that it's negative. Define A=X-B. Suppose that A is not positive. (This will lead to a contradiction). It's not too hard to see that A doesn't have any negative subsets, but we can still pick a subset E_0\subset A that has a negative size. Then we cut away disjoint pieces of E_0, denoted by E_1,E_2,\dots that have positive sizes. The goal is to show that E_0-\bigcup_{k=1}^\infty E_k is negative.
 
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Hmm, it tool me a while, but I've got it. Remember that m_k is the smallest integer such that there is a set E_k.

\mu (E_k)\geq \frac{1}{m_k}

Now take F measurable, if

\mu(F)\geq \frac{1}{m_k-1}

then m_k-1 is a smaller integer and F is another set for which

\mu(F)\geq \frac{1}{m_k-1}

This is in contradiction with the choice of m_k. Thus it must hold that

\mu(F)<\frac{1}{m_k-1}
 
You sir, are awesome. That was crystal clear. Thank you.
 
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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