covariance


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Related to covariance: Covariance matrix

covariance

[kō′ver·ē·əns]
(statistics)
A measurement of the tendency of two random variables, X and Y, to vary together, given by the expected value of the variable (X-X [OB ])(Y-Y [OB ]), where X [OB ] and Y [OB ] are the expected values of the variables X and Y respectively.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
We selected regression coefficients that provided nominal powers of .80 for the MBF procedure when covariance matrices were homogeneous across groups and completely balanced design (i.e., equal sample sizes and complete data).
Covariance Features for Trajectories (CFT) is constructed by adapting Ledoit-Wolf (LW) [1] and Oracle Approximating Shrinkage (OAS) [11] to time-series.
It is a model-based approach requiring the signal models and the noise statistics satisfying an optimality condition of minimum mean square error or minimum covariance. For estimating the position and speed of BLDC motor which is a nonlinear system, Extended Kalman Filter is used.
This study is based on a three-year (2011-2013) data set of eddy covariance measurements of C[O.sub.2] fluxes above a subtropical mountain forest in Taiwan.
The covariance matrix [W.sub.k] of measurement noise is controlled by the emitted power.
As discussed in [11], the unscented transformation is able to capture the higher-order moments caused by the nonlinear transform better than the Taylor-series-based approximations used in the Lyapunov-based covariance propagation in [5].
where [H.sub.k+1] is the m x n dimension measurement matrix, [Q.sub.k] is the covariance matrix of control noise, [R.sub.k+1] is the covariance matrix of observation noise, and [[PHI].sub.k+1,k] is the n x n dimension state-transition matrix.
Dr Annalisa Molini, a faculty member from the Civil Infrastructure and Environmental Engineering Department, is leading the team of researchers overseeing the eddy covariance monitoring campaign at the Mangrove National Park.
After selection of the best G matrix structure, the G matrix was fixed and the 12 covariance matrices for residual effects (R matrix) were tested.
When Table 1 and Table 2 are examined, it is seen that covariance method gives the lowest value and Yule-Walker method gives the highest value.
The CKF uses a set of cubature points with equal weights to approximate the a posteriori mean and covariance and achieves higher stability than the UKF [13-15].