Several mathematical models have been developed over the past few decades aiming to deliver estimations of solar irradiation components when only measurement of global irradiation is available.
In addition, recently a blind sensing spectrum technique has been introduced based on the wavelet transform which is able to deliver spectrum estimation over a wide-band of frequencies [43, 44].
However, in practice delivering this estimation with sufficient accuracy and robustness has proven challenging due to factors like poor measurement quality, variations in line construction and the inclusion of short cable runs in the transmission corridor [30, 31, 32].
Important questions include clarifying the best way of providing information, identifying the possible needs of different ethnic or religious groups, and sources of possible bias including the trustworthiness and reliability of those delivering risk estimation and screening.
Short-term block model values are generally available for each block and they deliver the prior estimation of block attributes (E-type estimate).
In order to create the quality model based on short-term model, the following strategy is employed: (1) Short-term block model values are generally available for each block and they deliver the prior estimation of block attributes (E-type estimate).
This chapter introduces an emerging process sensing method that integrates two measurement principles, spectroscopy and tomography, to deliver real-time estimation of distribution coupled with material identification.
Although HRI-based risk assessment method does not deliver a quantitative estimation for the likelihood of an exposed population undergoing a reverse health effect, it indeed provides a suggestion of health risk level due to exposure to pollutants (Chary et al. 2008).
Since no sample size calculation is available for that approach in Generalized Estimating Equations (GEE's) so far, the sample size calculation was based on linear multiple regression using the random model that supposes both target and predictor variables as random and should thus deliver a feasible estimation of the sample size required for GEE.
Our estimation procedure delivered useless estimates.
