Distribution of the sample median

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Warning:This page is currently in the "notes" stage, and is a staging area from conclusions drawn from Notes:Distribution of the sample median
  • This page is not "formal" yet. However the contents are accurate (to whatever they apply to) - I hope to distil the essence of an "ordered" unit from Alec's taxonomy of units and describe the median's distribution purely on that foundation, then anything which is itself ordinal (in theory both additive and real units) will be a corollary.

Set up

Let mN0 describe the size of the sample, n, by the relation: n=2m+1 - forcing n to be odd.

Let X1,,X2m+1 be i.i.d samples from a population distribution X; let M denote the median of the sample, X1,,Xn, and let F(r):=P[Xir]=P[Xr] for any i[Note 1] then:

  • For m=1 / n=3 we have: P[Mr]=F(r)2(2F(r)+3)
  • For m=2 / n=5 we have: P[Mr]=F(r)3(6F(r)215F(r)+10)
  • For m=3 / n=7 we have: P[Mr]=F(r)4(20F(r)3+70F(r)284F(r)+35)
  • For m=4 / n=9 we have: P[Mr]=F(r)5(70F(r)4315F(r)3+540F(r)2420F(r)+126)Caveat:[Note 2]

In general

Warning:This is written from memory, not from my notes! - Alec check the notes! Alec (talk) 21:57, 19 December 2017 (UTC)

In general I believe for mN0 given and n:=2m+1 that:

  • g(x):=mi=0(2m+1)Ci(x1)i
    - which we then expand and reverse the coefficients of to obtain the polynomials in the brackets with the xk factor removed for our P[Mr] equations above. We must then multiply this reversed polynomial by xm+1 I believe and job done!

Findings

The median seems to be a rather crappy estimator for the median of a distribution, for example with the Exponential distribution, so XExp(λ) for any λR and λ>0 Then E[X] is above the true median of X, which is ln(2)λ

but appears to show convergence

Continuing the exponential example, the following hold for all real λ>0, here M is the true median of the distribution, Mi is the median random variable for n=i samples and the constants are accurate to 2 s.f

  • 0.83E[M3]M
  • 0.88E[M5]M
  • 0.91E[M7]M
  • 0.93E[M9]M
  • 0.94E[M11]M
  • 0.95E[M13]M
  • 0.96E[M15]M


However X were normally distributed then the M2m+1s would not be biased and multiplying by these constants would be bad.

Ideas

  1. Use Alec's alternate expectation formula to try and find the expected value - again this is written from memory not notes but it states something like:
    • E[X]=0P[Xx]dx
      - but the requirement for X0 may not be needed (it could work for ALL RVs maybe - can't see my notes right now)... I'm not sure.

Notes

  1. Jump up As the Xi are i.i.d this will be the same for all i
  2. Jump up This one is predicted from a formula I found however this and further predictions seem to work. They have not been experimentally confirmed to the same high standard as previous results though

OLD WORK - unsaved addendum=

Reversing polynomial order

Let f(x):=ni=0aixi=a0+a1x+a2x2++an1xn1+anxn

[Note 1] be a polynomial in x

Our goal is to "reverse the coefficients" of f, to obtain a polynomial f such that:

  • f(x)=ni=0aixni=ni=0anixi=a0xn+a1xn1+a2xn2++an1x+an
    , perhaps better written as: f(x)=an+an1x+an2x2++a1xn1+a0xn

Process

Let f(x) be a nth-order polynomial over some field
TODO: TODO - just assume R for now
, then:
  • Define f1(x):=1xnf(x)=xnf(x)=ni=0aixin=a0xn+a1x1n+a2x2n++an1x1+an - Caveat:Provided x0
    • Define f2(x):=f1(1x) so f2(x)=ni=0aix(in)=a0xn+a1x(1n)+a2x(2n)++an1x(1)+an
      =a0xn+a1xn1+a2xn2++an1x1+an - which is what we want!

Notes

  1. Jump up Usually we use indexes starting at one, so for a polynomial of order n we would write n+1i=1aixi1 but the convenience outweighs the "minor gains" (if any) we'd make here

Found this in an old tab, I then made the above content, so adding it here