Importantly, however, choices in modeling affect what causes of a phenomenon of interest are revealed and accentuated (assuming a phenomenon typically has many causes).
Our choices in modeling the regulatory function of enhancers differ from these previous studies in a number of important respects.
Our likelihood model is based upon the multinomial distribution, which is a common choice in modelling count data.
The Weibull distribution was chosen for such comparisons because it is often used as the first choice in modelling cancer survival (Muszbek et al, 2012).
We describe the choices made in modelling our particular industrial setting using an example for a typical UK Round III offshore wind farm.
Though for tumors that undergo angiogenesis, a plateau in growth is not necessarily reached, the Gompertz cure is an appropriate choice in modeling the initial phase of the tumor growth with limited access to nutrients.
Fewer anatomical features need to be accounted for when modeling the mandible, therefore allowing us to focus on the choices in model resolution, material properties, and boundary conditions and their effects on analysis results.
Some subjectivity and arbitrariness in the study design is unavoidable, but this setup can give a realistic example of the bias that is induced when certain choices in model selection are based on optimizing performance on the learning data.
According to the previous analysis [ 17], within the category of static, deterministic, aggregate-level models, different choices in model structure lead to relatively modest differences in the estimated cost-effectiveness of rotavirus vaccination while intermediate "non-severe" epidemiologic outcomes vary more substantially depending on choices of model structure.
Though regression analysis has found its wide applications in the development of bicycle LOS models, it is not the best choice particularly in modeling the ordered response variables (e.g., perceived ratings = 1, 2,…, or 6 in the present case).
