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Given a matrix of observations (rows) x variables (columns), can we compute the correlation matrix of the rows, but corrected by the correlation matrix of the columns? The goal would be to avoid inflation in the correlation p values, since the t-test for Pearson correlation assumes that the columns/variables are uncorrelated, independent observations, i.e. that the correlation matrix of the columns is the identity matrix.

Intuitively, this could be accomplished with a weighted correlation, where e.g. if a pair of variables are nearly perfectly correlated, they would each be down-weighted by a factor of 2.

Given a matrix of observations (rows) x variables (columns), can we compute the correlation matrix of the rows, but corrected by the correlation matrix of the columns? The goal would be to avoid inflation in the correlation p values, since the t-test for Pearson correlation assumes that the columns/variables are uncorrelated, independent observations, i.e. that the correlation matrix of the columns is the identity matrix.

Intuitively, this could be accomplished with a weighted correlation, where e.g. if a pair of variables are nearly perfectly correlated, they would each be down-weighted by a factor of 2.

Edit: there is an exact null distribution for the correlations in the case where the columns are independent, but there are only two rows and this pair of rows is sampled from a bivariate normal distribution. I would like a null distribution for the opposite case, where the rows are independent, but the columns are sampled from a multivariate normal distribution.

Given a matrix of observations (rows) x variables (columns), can we compute the correlation matrix of the rows, but corrected by the correlation matrix of the columns? The goal would be to avoid inflation in the correlation p values, since the t-test for Pearson correlation assumes that the columns/variables are uncorrelated, independent observations.

Intuitively, this could be accomplished with a weighted correlation, where e.g. if a pair of variables are nearly perfectly correlated, they would each be down-weighted by a factor of 2.

Given a matrix of observations (rows) x variables (columns), can we compute the correlation matrix of the rows, but corrected by the correlation matrix of the columns? The goal would be to avoid inflation in the correlation p values, since the t-test for Pearson correlation assumes that the columns/variables are uncorrelated, independent observations, i.e. that the correlation matrix of the columns is the identity matrix.

Intuitively, this could be accomplished with a weighted correlation, where e.g. if a pair of variables are nearly perfectly correlated, they would each be down-weighted by a factor of 2.

Given a matrix of observations (rows) x variables (columns), can we compute the correlation matrix of the rows, but corrected by the correlation matrix of the columns? The goal would be to avoid inflation in the correlation p values, since the t-test for Pearson correlation assumes that the columns/variables are independent.

Intuitively, this could be accomplished with a weighted correlation, where e.g. if a pair of variables are nearly perfectly correlated, they would each be down-weighted by a factor of 2.

Given a matrix of observations (rows) x variables (columns), can we compute the correlation matrix of the rows, but corrected by the correlation matrix of the columns? The goal would be to avoid inflation in the correlation p values, since the t-test for Pearson correlation assumes that the columns/variables are uncorrelated, independent observations.

Intuitively, this could be accomplished with a weighted correlation, where e.g. if a pair of variables are nearly perfectly correlated, they would each be down-weighted by a factor of 2.