Sample standard deviation serially correlated normal data

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Discussion Overview

The discussion centers around the statistical properties of the sample standard deviation of a sequence of identically distributed normal random variables that exhibit some form of serial correlation. Participants are seeking references and insights into how serial correlation affects the sample standard deviation.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested

Main Points Raised

  • One participant requests references for understanding the statistical properties of the sample standard deviation in the context of serially correlated normal data.
  • Another participant provides a mathematical expression related to the expectation of the squared sum of the variables and introduces the concept of covariance between different variables.
  • A later reply reiterates the mathematical expression while expressing a specific interest in the statistical properties of the sample standard deviation, including its formula and the calculation of the sample mean.
  • One participant critiques the formatting of the LaTeX used in the mathematical expressions.

Areas of Agreement / Disagreement

The discussion does not appear to reach a consensus, as participants are exploring different aspects of the topic and expressing varying levels of clarity and focus on the sample standard deviation.

Contextual Notes

There are unresolved issues regarding the assumptions related to the statistical properties being discussed, particularly in the context of serial correlation and its impact on the sample standard deviation.

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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