In particular, we will develop the efficient estimating algorithm for (e_{m+1}), which fits the coefficients up to 7th order term in the expansion of the global error (E_{m+1}=phi(t_{m+1} -phi_{m+1}) about_{m+1} -phi_{m+1}ze h.
The classical generalized self-consistent model (GSCM) is recognized to be suitable and efficient for estimating the effective moduli of an isotropic composite consisting of an isotropic host matrix and an isotropic inclusion phase.
In general, we showed that ANN models could play a very important role for an efficient estimate of construction project costs in their early stages.
In contrast, crossing controls is less efficient for estimating a slope parameter for the treatment effect.
However, this option was rejected in favour of optimising the design for the MNL model, assuming that this design is also efficient for estimating a ML model.
It has been shown to be very efficient for estimating demographic parameters from genetic data and testing crop domestication models [ 24, 25].
We used a fractional factorial design and the techniques described in Street and Burgess [ 21] resulting in 12 binary choice sets which were 100% efficient for estimating the main effects.
An advantage of this applied model is that it allows the estimation of consistent and efficient estimates for unbalanced data structures by using maximum likelihood estimation [ 30].
In this paper, we use the SRM at Kochi Core Center (KCC), Japan, as an example to introduce new tool and procedure for accurate and efficient estimate of SRM sensor response.
The reduced form of the model required for consistent and efficient estimates was: fer t ha = π 1 ' X + e f (17) H M ha = π 2 ' X + e H, (18) e f, e H ~ BVN 0, 0, σ f 2, σ H 2, τ, (19 where τ is the correlation (measure of the extent to which the two errors covary), σ i 2 is the variance, and X is the union of exogenous variables in the system.
