The method used to generate the imputations (i.e., the propensity score method as implemented through the SAS PROC MI module), the degree of heterogeneity between the covariance
matrices (i.e., [[SIGMA].sub.2] = 2[[SIGMA].sub.1]), the total sample size (i.e., n = 100), the shape of the distribution of the measure variable (i.e., [[gamma].sub.1] = 0; [[gamma].sub.2] = 0), the missing data mechanisms (i.e., MAR) and the patterns of missingness (i.e., monotone) were kept constant in the study.
Suppose that[mathematical expression not reproducible], is the LW covariance
estimation of the image of set U.
The measurement noise covariance
matrix also has a diagonal structure giving the current measurement and A/D convertor error ([r.sub.ii] is the variance of [v.sub.k]) given as [R.sub.k] = diag [[r.sub.11] [r.sub.22] [r.sub.33]].
The postprocessing of the eddy covariance
data was done with EddyPro (version 6.0.0, Licor, USA).
Utilizing all the states and model probabilities from last recurrence, the computation of input state [[??].sub.0j] (k - 1) and covariance
[P.sub.0j] (k - 1) of model j can be express as
[R.sub.e] = 6378 x [10.sup.3] m is the Earth's radius, [e.sub.3] = [[0,0,1].sup.T] is the third standard unit vector [R.sup.3] in and [xi] is the random acceleration vector due to modeling uncertainties and disturbances, and it is assumed that the noise vector is a zero-mean Gaussian process, [mathematical expression not reproducible] is the positive definite process noise covariance
[P.sup.a[k].sub.t-1] is an augmented covariance
matrix which contains robot covariance
, control noise, and observation noise, and its dimension is 7 x 7.
"We know that mangrove forests are extremely 'good' at storing carbon," said PhD student Saverio Perri, who designed and coordinated the eddy covariance
tower deployment at the Abu Dhabi Mangrove National Park.
The fixed curve was modeled by second and third order polynomial regressions using 12 matrix structures of the random variance and covariance
matrix (G), maintaining the residual effects matrix (R) always equal to the VC.
Table 4-7 shows that the CRLB value is lower in the covariance
The key problem in nonlinear Kalman filter is to calculate the intractable nonlinear Gaussian weighted integral as [mathematical expression not reproducible], where x [member of] [R.sup.n], g(x) represents the arbitrary nonlinear function, and N(x; [??], [P.sub.x]) denotes the Gaussian distribution with mean x and covariance
The PCA loading matrix P can be obtained by eigenvalue decomposition on the covariance