A commonly taught scientific method for building mathematical models uses finite computations to approximate the curve of a specified type that best fits the data, without checking whether any such best-fitting curve exists: not every regression objective need have a global unconstrained minimum. One counterexample will confute its theoretical foundation: any triple of points with super-exponen...

soil water retention curve (swrc) is important in the studies of soil and water relationship, soil conservation, irrigation scheduling, drainage, solute transport, plant growth and crop water stress. the performance of 10 models (simons et al., libardy et al., campbell, farrell and larson, van genuchten, brooks and corey, driessen, exponential bruce-luxmore, power bruce-luxmore, and rogowski) w...

We propose the use of the latent change and latent acceleration frameworks for modeling nonlinear growth in structural equation models. Moving to these frameworks allows for the direct identification of rates of change and acceleration in latent growth curves-information available indirectly through traditional growth curve models when change patterns are nonlinear with respect to time. To illu...

decline curve analysis has some advantages over transient well test analysis in which it is not required to shut-in the well and also wellbore storage effects do not exist. few studies have been done on decline curve analysis of naturally fractured reservoirs but there are even some limitations with available models. on the other hand well test could be expensive and in some operational conditi...

Background: Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders in pregnancy, which is associated with serious complications. In the event of early diagnosis of this disease, some of the maternal and fetal complications can be prevented. The aim of this study was to early predict gestational diabetes mellitus by two statistical models including artificial neural ne...

Repeated measures and repeated events data have a hierarchical structure which can be analysed using multilevel models. A growth curve model is an example of a multilevel random coefficients model, while a discrete-time event history model for recurrent events can be fitted as a multilevel logistic regression model. The paper describes extensions to the basic growth curve model to handle autoco...