Several works have been done for these estimations; for example, paper [9] provides a method by employing digital maps, and [21] focuses the estimation on using SAR images.
Thus, the component biomass estimations, for example branch or crown biomass, bole biomass, and bark biomass, are important to account for the variability within the tree.
A more explicit approach should be followed to deal with uncertainties in the estimations, for example using the Bayesian methods that allow explicitly for the integration of subjective assessments, multivariate sensitivity analyses (i.e. simulate the estimates by imputing the plausible ranges of values of different parameters) should be employed [ 14].
In fact, (widehat {GS}) is inconsistent in the case of K=∞ or K=K n converging to ∞ too fast, and furthermore one cannot use the modern penalized estimation, for example lasso, to estimate p k, since there is no sparsity structure here.
These gains are more pronounced if less pilots are available for channel estimation, for example, when a fixed number of pilots is split between parallel data streams.
Take the velocity estimation for example, from (13), we can see that the target velocity estimation accuracy depends on the choice of velocity step Δv= v i - vi-1.
Another advantage of this type of methods is the use of readily available information from the bitstream that is significant for the quality estimation, for example, the motion vectors, coding modes, and quantization parameter values.
Though the moment method is not the optimal tool in statistical inference, as compared to maximum likelihood estimation for example, its controlled use in conjunction with the Anderson-Darling and Cramér-von Mises goodness-of-fit statistics remains a first choice in applications depending heavily on moments as the one considered in Hürlimann [41 43].
It is well known that single-gene estimation (for example, ER) of individual pathway activity is not accurate enough to predict treatment outcomes (for example, response to ET).
Accordingly, the term high-risk patient was based on very individual criteria rather than on established tools for perioperative risk estimation (for example, EuroSCORE, STS Risk Score).
While manifestations of sampling uncertainty can be identified by the variability in results from multiple samples used for the same estimation (for example, the replicates of a laboratory experiment), it may be more difficult to recognize sampling uncertainty in a single sample which is often the case for hospital comparisons.
