A new log-location regression model with influence diagnostics and residual analysis. Facta Univ. Ser. Math. Inf. 2018, 33, 417–449. [Google Scholar] Altun, E.; Yousof, H.M.; Chakraborty, S.; Handique, L. Zografos-Balakrishnan Burr XII distribution: Regression modeling and applications. ...
2.3.2. Mortality Sub-Models The transition sub-model is coupled with Poisson regression models used to estimate the number of survivors of each age category at the end of the simulation. A Kaiser–Permanente (KP) Medical Care Program Cohort study provided person-years and number of deaths due...
The above ℓ𝑛(𝜉 ) can be maximized numerically via “SAS (PROC NLMIXED)” or “R (optim)” or “Ox program (via sub-routine MaxBFGS)”, among others. The components of the score vector U ( ξ _ ) = ∂ ℓ ∂ ξ _ = ( ∂ ℓ n ( ξ _ ) ∂ θ , ∂ ...
Based on the p-values, the significant factors in the quadratic model of FV were obtained, i.e., X1, X2, X3, X1X3 and (X3)2. Based on the least squares method, the regression coefficients of factors can be determined, and the reasonable second-order polynomial equation in terms of ...
The purposed determination showed linear dependence in the concentration range of Anti-sarcosine IgG labeled gold nanoparticles from 0 to 1000 µg/mL and the regression equation was found to be y = 3.8x − 66.7 and R2 = 0.99. Performed ELISA confirmed the ability of Anti-sarcosine IgG ...
In addition, our predictor was intended to be capable of performing large-scale calculations in a reasonable amount of time. Our method uses a multiple linear regression model to combine a weighted MM/PBSA approach with knowledge-based terms to increase correlation to experimental ∆∆G values ...