Some estimation algorithms presented in the literature were compared in this paper, and their behavior was evaluated for some test cases.
Some estimation problems are found to be naturally tractable using only characteristic functions, such as the multivariable linear system with additive Cauchy measurement and process noise.
Some estimation algorithms, based on regression and auto-regression, for parabolic distributed parameters systems, are developed.
Some estimation is also needed for tracing the proximal part of the humerus.
Some estimation could be given by manipulations of this ratio using PAML or SELECTON.
Some estimation problems were also seen with the Robins & Tsiatis methods when used with a Cox, Weibull or exponential test.
Some estimation methods proceed by maximizing a posterior distribution or a likelihood function, and are amenable to an exact reconstruction of the optimal tree, but Bayesian phylogenetic analyses generally produce posterior distributions that are best explored by generating posterior samples.
If the polarized states are not exactly estimated, that is, some estimation errors are introduced, we also consider the case of two-radar-member in the RSN for simplifying the complexity of analysis.
A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose.
However, blind receivers have to estimate symbol rate that always conveys some estimation error.
Although the PTFT is the maximum likelihood estimator, it is still possible to have some estimation errors, especially when the signals are corrupted by heavy noises.
