In this paper we propose the GHADA risk management model that is based on the generalized hyperbolic (GH) distribution and on a nonparametric adaptive methodology. Compared to the normal distribution, the GH distribution possesses semi-heavy tails and represents the financial risk factors more appropriately. The nonparametric adaptive methodology has the desirable property of estimating homogen...

background: in survival studies when the event times are dependent, performing of the analysis by using of methods based on independent assumption, leads to biased. in this paper, using copula function and considering the dependence structure between the event times, a parametric joint distribution has made fitting to the events, and the effective factors on each of these events would be determ...

determination of the amount of sugar in sugar beet is usually accomplished by polarimetric method in sugar industry. this method is not accurate due to impurities such as sodium, potassium and amino-nitrogen that are present and might cause some errors in the evaluation of the technical quality of sugar beet. in this research, 7309 samples of sugar beet from two semi- arid county, isfahan and c...

Semi-Markov models are a generalisation of Markov models that explicitly model the state-dependent sojourn time distribution, the time for which the system remains in a given state. Markov models result in an exponentially distributed sojourn time, while semi-Markov models make it possible to define the distribution explicitly. Such models can be used to describe the behaviour of manoeuvring ta...

A model for dihadron fragmentation functions is briefly outlined, that describes the fragmentation of a quark in two unpolarized hadrons. The parameters are tuned to the output of the PYTHIA event generator for two-hadron semi-inclusive production in deep inelastic scattering at HERMES. Then predictions are made for the unknown polarized fragmentation function and the related single-spin asymme...

Semi-supervised learning plays an important role in large-scale machine learning. Properly using additional unlabeled data (largely available nowadays) often can improve the machine learning accuracy. However, if the machine learningmodel is misspecified for the underlying true data distribution, the model performance could be seriously jeopardized. This issue is known as model misspecification...