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 matrix.

[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 method.

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 [P.sub.x].

The PCA loading matrix P can be obtained by eigenvalue decomposition on the

covariance matrix [[SIGMA].sub.X].