The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development simple, non-invasive models. However, different were utilized conjunction with various data, either alone or combination other biophysical and lifestyle variables. It is essential to assess impacts chosen models using simple measurements. We developed tested 13 methods neura...

Dynamic random network models are presented as a mathematical framework for modelling and analyzing the time evolution of complex networks. Such framework allows the time analysis of several network characterizing features such as link density, clustering coefficient, degree distribution, as well as entropy-based complexity measures, providing new insight on the evolution of random networks. So...

We consider an environment in which agents face various choice sets, assembled from a finite universe of objects, and choose a single object each time a choice set is presented to them. Models for probabilistic discrete choice give, for each choice set, a discrete probability distribution over that choice set. We use methods of Bayesian model comparison to measure the empirical plausibility of ...

Time is an important aspect of health economic evaluation, as the timing and duration of clinical events, healthcare interventions and their consequences all affect estimated costs and effects. These issues should be reflected in the design of health economic models. This article considers three important aspects of time in modelling: (1) which cohorts to simulate and how far into the future to...

Background Between 1990 and 2015, under-5 mortality rate (U5MR) declined by 53%, from an estimated rate of 91 deaths per 1000 live births to 43, globally. The aim of this study was to determine the share of health research systems in this decrease alongside other influential factors.   Methods We used random effect regression models including the ‘random intercept’ and ‘random intercept and ran...

Discrete choice models have played an important role in transportation modeling for the last 25 years. They are namely used to provide a detailed representation of the complex aspects of transportation demand, based on strong theoretical justiications. Moreover, several packages and tools are available to help practionners using these models for real applications, making discrete choice models ...

We illustrate and discuss several general issues associated with the random component of utility, or more generally ‘‘unobserved variability’’. We posit a general conceptual framework that suggests a variance components view as an appropriate structure for unobserved variability. This framework suggests that ‘‘unobserved heterogeneity’’ is only one component of unobserved variability; hence, a ...

SUMMARY. A rich class of parametric models is proposed for discrete choice data based on the scale mixtures of multivariate normal distributions. With special connections to multinomial probit, the new models can be implemented in a Bayesian framework without much diiculty. The proposed class of models can be extended to panel data where accounting for heterogeneities is needed. This is done by...

We show that the distributions of random coefficients in various discrete choice models are nonparametrically identified. Our identification results apply to static discrete choice models including binary logit, multinomial logit, nested logit, and probit models as well as to dynamic programming discrete choice models. In these models the only key condition we need to verify for identification ...