Expected Value and covariance of matrix

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To find the expected value E[A] of a 2x1 matrix A, the mean is calculated by averaging its elements, resulting in a 1x1 matrix. The covariance of matrix A can be computed using the formula cov(A) = E[(A - E[A])(A - E[A])^{T}], but requires a sufficient dataset to provide meaningful results. If A consists of only two elements, calculating covariance may not be statistically valid without additional data points. The discussion emphasizes the importance of defining the distribution of the matrix elements for accurate calculations. Understanding these concepts is crucial for proper statistical analysis.
tommyhakinen
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I have a 2x1 matrix A. I would like to find out E[A] which is the mean of the matrix. How do I do this? what is the dimension of the resultant matrix? using this E[A], I am going to find the covariance of matrix A by this formula

cov(A) = E[(A - E[A])(A - E[A])^{T})

could someone please enlighten me? thank you in advance.
 
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tommyhakinen said:
I have a 2x1 matrix A. I would like to find out E[A] which is the mean of the matrix. How do I do this? what is the dimension of the resultant matrix? using this E[A], I am going to find the covariance of matrix A by this formula

cov(A) = E[(A - E[A])(A - E[A])^{T})

could someone please enlighten me? thank you in advance.


It depends on what you mean by the "mean of a matrix". I assume you are trying to find out what the elements of the matrix should be. Well if each element has a Gaussian distribution centered around 0, then the "average" vector should have all of its entries as 0. But this depends on your choice of distribution, mean for the element entries, and standard deviation.

Try to better define what you are saying
 
brydustin said:
I think you had better look at this:
http://www.itl.nist.gov/div898/handbook/pmc/section5/pmc541.htm
to get started.

Thanks for the reply. but if my matrix A is a 2x1 vector which only has two elements, is it possible to find the covariance or i have to have the set of data in order to get the covariance?
 
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