Distribution of the sample median
- 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.
Contents
[hide]Set up
Let m∈N0 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[Xi≤r]=P[X≤r] for any i[Note 1] then:
- For m=1 / n=3 we have: P[M≤r]=F(r)2(−2F(r)+3)
- For m=2 / n=5 we have: P[M≤r]=F(r)3(6F(r)2−15F(r)+10)
- For m=3 / n=7 we have: P[M≤r]=F(r)4(−20F(r)3+70F(r)2−84F(r)+35)
- For m=4 / n=9 we have: P[M≤r]=F(r)5(70F(r)4−315F(r)3+540F(r)2−420F(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 m∈N0 given and n:=2m+1 that:
- g(x):=m∑i=0(2m+1)Ci∗(x−1)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[M≤r] 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 X∼Exp(λ) for any λ∈R and λ>0 Then E[X] is above the true median of X, which is ln(2)λ
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
- 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[X≥x]dx- but the requirement for X≥0 may not be needed (it could work for ALL RVs maybe - can't see my notes right now)... I'm not sure.
- E[X]=∫∞0P[X≥x]dx
Notes
- Jump up ↑ As the Xi are i.i.d this will be the same for all i
- 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):=n∑i=0aixi=a0+a1x+a2x2+⋯+an−1xn−1+anxn
Our goal is to "reverse the coefficients" of f, to obtain a polynomial f′ such that:
- f′(x)=n∑i=0aixn−i=n∑i=0an−ixi=a0xn+a1xn−1+a2xn−2+⋯+an−1x+an, perhaps better written as: f′(x)=an+an−1x+an−2x2+⋯+a1xn−1+a0xn
Process
Let f(x) be a nth-order polynomial over some field- Define f1(x):=1xnf(x)=x−nf(x)=∑ni=0aixi−n=a0x−n+a1x1−n+a2x2−n+⋯+an−1x−1+an - Caveat:Provided x≠0
- Define f2(x):=f1(1x) so f2(x)=∑ni=0aix−(i−n)=a0xn+a1x−(1−n)+a2x−(2−n)+⋯+an−1x−(−1)+an
- =a0xn+a1xn−1+a2xn−2+⋯+an−1x1+an - which is what we want!
- Define f2(x):=f1(1x) so f2(x)=∑ni=0aix−(i−n)=a0xn+a1x−(1−n)+a2x−(2−n)+⋯+an−1x−(−1)+an
Notes
- Jump up ↑ Usually we use indexes starting at one, so for a polynomial of order n we would write ∑n+1i=1aixi−1 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