Abstract
The nonlinear PET model based on Newton's law of cooling can be used to estimate body temperature in cattle, T b challenged by hot cyclic chamber temperatures, T a . The PET model has four biologically meaningful parameters: K, the thermal constant; Δ, the difference between T b and adjusted T a ; Υ the proportion of variation in T b comparable to variation in Ta ; T bini, the initial body temperature. The two parameters Y and Δ are highly correlated in the current version of the model. This study looks at other ways to parameterize the PET model in an effort to reduce the correlation between parameters and improve nonlinear behaviors, such as parameter-effects curvature, bias, excess variance and skewness.
Keywords
Nonlinear, PET model, parameterize, nonlinear behavior, curvature, bias, excess variance, skewness, correlation
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Wu, J.; Parkhurst, A.; Eskridge, K.; Travnicek, D.; Brown-BrandI, T.; Eigenberg, R.; Hahn, G. L.; Nienaber, J.; Mader, T.; and Spiers, D.
(2003).
"COMPARING CORRELATED PARAMETER ESTIMATES FOR NONLINEAR PET MODEL,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1193
COMPARING CORRELATED PARAMETER ESTIMATES FOR NONLINEAR PET MODEL
The nonlinear PET model based on Newton's law of cooling can be used to estimate body temperature in cattle, T b challenged by hot cyclic chamber temperatures, T a . The PET model has four biologically meaningful parameters: K, the thermal constant; Δ, the difference between T b and adjusted T a ; Υ the proportion of variation in T b comparable to variation in Ta ; T bini, the initial body temperature. The two parameters Y and Δ are highly correlated in the current version of the model. This study looks at other ways to parameterize the PET model in an effort to reduce the correlation between parameters and improve nonlinear behaviors, such as parameter-effects curvature, bias, excess variance and skewness.
