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As stated in thisthis question, the maximum rank of covariance matrix is $n-1$ where $n$ is sample size and so if the dimension of covariance matrix is equal to the sample size, it would be singular. I can't understand why we subtract $1$ from the maximum rank $n$ of covariance matrix.

As stated in this question, the maximum rank of covariance matrix is $n-1$ where $n$ is sample size and so if the dimension of covariance matrix is equal to the sample size, it would be singular. I can't understand why we subtract $1$ from the maximum rank $n$ of covariance matrix.

As stated in this question, the maximum rank of covariance matrix is $n-1$ where $n$ is sample size and so if the dimension of covariance matrix is equal to the sample size, it would be singular. I can't understand why we subtract $1$ from the maximum rank $n$ of covariance matrix.

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amoeba
amoeba
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why Why is the rank of covariance matrix is at most n$n-11$?

As stated in this question, the maximum rank of covariance matrix is n-1$n-1$ where n$n$ is sample size and so if the dimension of covariance matrix is equal to the sample size, it would be singular. I can't understand why we substract 1subtract $1$ from the maximum rank $n$ of covariance matrix.

why the rank of covariance matrix is at most n-1?

As stated in this question, the maximum rank of covariance matrix is n-1 where n is sample size and so if the dimension of covariance matrix is equal to the sample size, it would be singular. I can't understand why we substract 1 from the maximum rank of covariance matrix.

Why is the rank of covariance matrix at most $n-1$?

As stated in this question, the maximum rank of covariance matrix is $n-1$ where $n$ is sample size and so if the dimension of covariance matrix is equal to the sample size, it would be singular. I can't understand why we subtract $1$ from the maximum rank $n$ of covariance matrix.

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user3070752
user3070752
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why the rank of covariance matrix is at most n-1?

As stated in this question, the maximum rank of covariance matrix is n-1 where n is sample size and so if the dimension of covariance matrix is equal to the sample size, it would be singular. I can't understand why we substract 1 from the maximum rank of covariance matrix.