- #1
estebanox
- 26
- 0
Consider a sample consisting of {y1,y2,...,yk} realisations of a random variable Y, and let S(k) denote the variance of the sample as a function of its size; that is
S(k)=1/k( ∑ki=1(yi−y¯)2)
for y¯=1/k( ∑ki=1 yi)
I do not know the distribution of Y, but I do know that S(k) tends to zero as k tends to infinity.
Suppose that I can increase the sample size gradually, so that I can calculate a sequence of variances {S(1),S(2),...,S(k)} for any strictly positive integer k.
I would like to determine the sample size that guarantees a minimum (arbitrary) 'degree of convergence'; in other words, I would like to determine the minimum value of k for which we expect that S(k+1)=S(k)±ϵ , for some small ϵ.
Ideas? I'm not interested in the analytical answer to this question, but rather something that I can implement numerically.
NOTE: This question has also been asked here
S(k)=1/k( ∑ki=1(yi−y¯)2)
for y¯=1/k( ∑ki=1 yi)
I do not know the distribution of Y, but I do know that S(k) tends to zero as k tends to infinity.
Suppose that I can increase the sample size gradually, so that I can calculate a sequence of variances {S(1),S(2),...,S(k)} for any strictly positive integer k.
I would like to determine the sample size that guarantees a minimum (arbitrary) 'degree of convergence'; in other words, I would like to determine the minimum value of k for which we expect that S(k+1)=S(k)±ϵ , for some small ϵ.
Ideas? I'm not interested in the analytical answer to this question, but rather something that I can implement numerically.
NOTE: This question has also been asked here