When the number and positions of the impulsive noise samples are not known, h t in (38) is not known for any t; therefore, we assume the maximum possible number of impulsive noise samples per block, i.e., k = ⌊ n − l 2 ⌋ as given in (96) in Appendix Appendix 1.
In order to plot the cdf, the function may be computed for some specific parameter values, as given in the Appendix.
It is clear that, based on Model 1, the odds ratio in the kth center, as given in the appendix 4, is exp(β + b 1k - b 2k), which can be written as C × exp b 1k - b 2k) where C = exp.This indicates that the odds ratio in each center is absolutely independent of α and u k.
References to over 80 studies that have collected relevant data, as well as to reviews of the subject, are given in Appendix II (www. bjcancer.com).
The parameters of the system under study are given in Appendix A. As discussed earlier, the controller is designed for Area-1 where cumulative disturbance is maximum.
The equations for obtaining a channel estimate from two consecutive training symbols are given in Appendix A. As in the case of DF, the two options "selective" and "antenna diversity" exist.
The details of how parameters a and b are evaluated are given in Appendix A. As mentioned above, the regression model relies on the observational data to evaluate the hypothesis function parameters.
The derivation of the regularity condition, as a precondition to the CRVB, is given in Appendix 3. The entries of the Fisher information matrix F are derived, using the the log likelihood function in (24), according to F ( i, l ) = − E ∂ 2 ln l ( b ~ | v ) ∂ v i ∂ v l ∀ i, l ∈ { 1, 2, 3 } (34).
