Undergrad Correlation Limits without Probability

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The discussion centers on the correlation limits of binary sequences based on their lengths and counts of 1's. It establishes that the maximum correlation between two sequences A and B can reach ±1 only when the counts of 1's are equal; otherwise, the correlation is constrained to values less than 1. The correlation is defined through the counts of coincidences and anti-coincidences, leading to a derived formula that reflects the relationship between the sequences. Participants clarify that the sequences must be of equal length for the correlation to be valid. The conversation concludes with a recognition of the need for precise definitions in the context of correlation.
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I'm reposting this piece of logic because my earlier attempts to get it across failed - nobody got what I was saying.

This is about binary sequences like ##010110100111001001...##.
All we need to know about a particular sequence is its length, ##N## and the number of 1's it contains. The logic applies to all permutations of any given sequence.
<br /> \begin{align*}<br /> \overbrace{<br /> \underbrace{1111111111...}_\text{count of 1s $=1_K$} \<br /> \underbrace{0000000000...}_\text{count of 0s $=0_K$}<br /> }^\text{length of K is $N=0_K+1_K$}<br /> \end{align*}<br />
The question is - given two sequences labelled A and B with 1 counts of ##1_A## and ##1_B##, what is the maximum correlation possible between any permutations of A and B ? The answer is obviously ##\pm1##. But this extreme can only be reached by a small subset of all sequences, the remainder having limits ##|\rho_M|<1##.

The comparison of two sequences means arranging then in two rows and counting the coincidences 0,0 and 1,1 denoted by ##S_{AB}##. The number of anti-coincidences ##A_{AB}## is obviously ##N-S_{AB}##. Finally, the correlation between A and B is defined in terms of coincidence counts as ##\mathcal{C}_{AB}= (S_{AB}-A_{AB})/(S_{AB}+A_{AB})=(2S_{AB}-N)/N ## which is in ##[-1,1]##.

So far everything is definitions and notation. Now comes the crucial ansatz

If ##1_A<1_B## then the maximum number of 1,1 coincidences is ##1_A## and the maximum number of 0,0 coincidences is ##0_B=N-1_B## and the maximum number of symmetric coincidences possible is ##S_{AB}= 1_A+0_B = 1_A-1_B +N## .

Putting this into the correlation gives ##1-\frac{2(1_B-1_A)}{N} \le 1 ##. This can only be 1 if ##1_A=1_B##. In all other cases the extreme cannot be achieved because there will be anti-coincidences and coincidences.

Is there a fault in the logic ? I suspect this could be more elegantly reasoned

Has anyone got a reference to something similar ?
 
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You appear to be implicitly assuming that both sequences have the same length. I will maintain that assumption.
If ##1_A=1_B## then by sorting both sequences in descending order we see that the sorted sequences are identical, as the first ##1_A## elements of both are 1 and the next ##N-1_A## elements of both are 0. So all pairs match.

Assume WLOG that ##1_A\leq 1_B## and let ##1_B-1_A=h##. Then if both sequences are sorted in descending order we have:
  • the first ##1_A## elements matching as (1,1), where the first number indicates the ##A## value and the second the ##B## value
  • the next ##h## elements mismatching as (0,1)
  • the last ##N-1_B=N-1_A-h## elements matching as (0,0)
So ##S_{AB}=N-h,A_{AB}=h## and the correlation statistic is ##1-2h/N##. Since ##0\leq h\leq N##, this statistic is in ##[-1,1]## and it is 1 iff ##h=0## (in which case the two sorted strings are identical) and -1 iff ##h=N## (in which case string ##A## is all 0s and string ##B## is all 1s).
 
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andrewkirk said:
You appear to be implicitly assuming that both sequences have the same length. I will maintain that assumption.
If ##1_A=1_B## then by sorting both sequences in descending order we see that the sorted sequences are identical, as the first ##1_A## elements of both are 1 and the next ##N-1_A## elements of both are 0. So all pairs match.

Assume WLOG that ##1_A\leq 1_B## and let ##1_B-1_A=h##. Then if both sequences are sorted in descending order we have:
  • the first ##1_A## elements matching as (1,1), where the first number indicates the ##A## value and the second the ##B## value
  • the next ##h## elements mismatching as (0,1)
  • the last ##N-1_B=N-1_A-h## elements matching as (0,0)
So ##S_{AB}=N-h,A_{AB}=h## and the correlation statistic is ##1-2h/N##. Since ##0\leq h\leq N##, this statistic is in ##[-1,1]## and it is 1 iff ##h=0## (in which case the two sorted strings are identical) and -1 iff ##h=N## (in which case string ##A## is all 0s and string ##B## is all 1s).
Yes the sequences being compared have the same length. I agree with your derivation. It looks a bit shorter than mine.
A more general expression for the maximum correlation is
##-1\le 1-\frac{2|1_B-1_A|}{N}\le 1## which is symmetric under interchange of A and B and does not require the assumption ##1_A<1_B##.
 
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What is the precise definition of the correlation between 2 given sequences ?
 
The OP defines what they want the term to mean in this context at the end of their 4th paragraph. It is different from the usual meaning of correlation.
 
Sorry, I don't know how I missed it, thanks.
 
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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