In the next step of the empirical investigation, we proceed to the estimation of nonlinear causality test (DP test), for our model following Bekiros and Diks (2008a, b).
Going one step further to the solution process of the parametric distance model, we proceed to the estimation of total deviations from the AHP weights for each dispatching rule, while the parameter (theta ) is (0le theta le theta _{max } ).
We proceed to parameter estimation using jointly the 36 statistics calculated for those 4 loci.
Once we had an initial explicit shape estimation, we will proceed to 3D depth estimation.
We proceed to consider causal estimation only using the genetic variants in and around the CASR gene region.
In this section, we will first review some existing results on state estimation methods designed to address the network-induced phenomena mentioned above, and then proceed to investigate some distributed estimation approaches for networked systems.
end{aligned} (28) We will now proceed to employ the quantum estimation techniques necessary to find the ultimate bounds on the measurement errors we have explicitly found above.
One possible approach is to consider such vectors as deterministic but unknown parameters and proceed to the joint ML estimation of the parameter set, with.
Then, it proceeds to the estimation of an effective value of the seismic coefficient khE, as a percentile of khmax, to be used with a requirement for pseudo-static factor of safety greater or equal to 1.0.
Once we check for the balancing properties and common support across the treatment and comparison group, we proceed to use the nearest neighbour estimation matching procedure.
