sample covariance मीनिंग इन हिंदी

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Commonly used estimators include sample covariance.
As far as sample covariance matrices are concerned, a theory was developed by Mar enko and Pastur.
The product in the final line is therefore zero; there is no sample covariance between different principal components over the dataset.
Similarly, the intrinsic inefficiency of the sample covariance matrix depends upon the Riemannian curvature of the space of positive-define matrices.
Due to their ease of calculation and other desirable characteristics, the sample mean and sample covariance are widely used in statistics and applications to numerically represent the distribution.
simply by replacing the state covariance Q in Kalman gain matrix K by the sample covariance C computed from the ensemble members ( called the " ensemble covariance " ).
The root mean square residual ( RMR ) and standardized root mean square residual ( SRMR ) are the square root of the discrepancy between the sample covariance matrix and the model covariance matrix.
It can be seen in Eq . ( 4 ) that the penalty function of Eq . ( 9 ) is minimized by approximating the signal model to the sample covariance matrix as accurate as possible.
Next, we apply a property of least square regression models, that the sample covariance between \ hat { Y } _ i and Y _ i-\ hat { Y } _ i is zero.
where " n " is the sample size, \ overline { \ mathbf x } is the vector of column means and { \ mathbf S } is a m \ times m sample covariance matrix.