Especially, Cao and Huang in 2011 developed a three-step method (ThSM) to guarantee that no infeasible solutions be included in the solutions of an interval-parameter programming model where all coefficients are assumed to obey normal or uniform distribution (Cao and Huang [2011]; Huang and Cao [2011]).
To produce relative weights, stepwise logistic regression was done for each participant with adjustment of thresholds for retention of predictors - if necessary - until a solution was found in the model where regression coefficients did not have high standard errors.
This contrasts with standard generalized linear models (GLMs), where coefficients and covariances are difficult to interpret and cannot be compared across responses that are modeled on different scales and with nonlinear link functions.
The results demonstrated that the Pb II) adsorption on the selected pine sawdust is well fitted by Langmuir model where the coefficient of determination is equal to 0.9994 and the adsorption capacity is about 16 which proved a good affinity and monolayer adsorption of Pb II) onto sawdust adsorbent.
Models were run adjusting for age at first breast cancer diagnosis and included an 'offset term' (i.e., log weight 'covariate' in the model where the coefficient of this log weight is fixed at one [ 16]), taking into account the sampling probabilities of the counter-matching.
First, in our multilevel modelling we fitted a random coefficient model where we allowed the coefficients for employment status to vary over the years (models allowing all variables (age, socio-economic characteristics and employment status) to vary in the multilevel model unfortunately did not converge).
Understanding these processes requires the development of a detailed chemical model, where the rate coefficients of the individual reactions have been measured in the laboratory.
This approach is based on an error correction model where the adjustment coefficients switch between regimes.
We assume a block fading Rayleigh channel model, where the channel coefficients are constant during each time slot and change independently from slot-to-slot.
Also, the assumption of block fading model where the channel coefficients stay constant over an entire block of communication allows for completing the selection process (scheduling) between users before their channels change again.
