Point estimates and confidence intervals for each time point were generated based on the fitted longitudinal model, using the ESTIMATE statement in the MIXED procedure in SAS.
Using the estimate (2.3) for and, we have (2.16).
By using the estimate (2.25), we deduce the existence of functions, such that in and in.
Using the estimate (5.6) from Theorem A.3 we have the first main result.
By using the estimate (4.8), by virtue of Theorem A, we get the uniform estimate (4.9).
Estimate the pure noise content, W H [2,..., k], using the estimate in (1) 3.
Using the estimate and (5.1) again, we have the estimate of (u_{n}) in (W_{0}^{1,p}(Omega)). □.
So, for small enough and using the estimate (35), there exist (depending on κ ), such that: (43).
However, we can do better, using the estimate which exploits the value of (infvarphi"), i.e., the estimate in (8).
For (2.4), using the estimate of (2.2), we can prove (2.4) exactly in the same way as deriving (2.3).
At the prediction step, the state and covariance are predicted using the estimate of the previous step.
