Sample standard deviation serially correlated normal data

AI Thread Summary
The discussion focuses on the statistical properties of the sample standard deviation for identically distributed normal random variables that exhibit serial correlation. A user seeks references regarding how serial correlation affects the calculation and properties of the sample standard deviation. The conversation includes mathematical expressions for variance and covariance, emphasizing the need to account for serial correlation in the analysis. There is a request for clarification on the formula for sample standard deviation and the mean. Overall, the thread highlights the complexity of analyzing standard deviation in the presence of serial correlation.
rhz
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Hi,

Can anyone point me to a reference for the statistical properties of the sample standard deviation of a sequence of identically distributed normal random variables subject to some form of serial correlation?

Thanks,

rhz
 
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( ∑ Xk)2 = ∑∑XkXj
Take expectation and subtract out the mean squared and you will have:

2 + ∑∑(k≠j) cov(k,j)

cov(k,j) is the covariance of XkXj.
 
mathman said:
( ∑ Xk)2 = ∑∑XkXj
Take expectation and subtract out the mean squared and you will have:

2 + ∑∑(k≠j) cov(k,j)

cov(k,j) is the covariance of XkXj.

Hi,

OK, but I'm interested in the statistical properties of the sample standard deviation:

\sqrt{\hat\sigma^2} = \sqrt \left ( \frac{1}{N-1}\sum^{N-1}_{i=0}(x_i-\hat{\mu})^2 \right )
\hat\mu = \frac{1}{N}\sum^{N-1}_{i=0}x_i

Thanks.
 
Fix your latex!
 
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