Recent studies have shown the potential of Reinforcement Learning (RL) algorithms in tuning parameters Model Predictive Controllers (MPC), including weights cost function and unknown MPC model. However, a framework for easy straightforward implementation that allows training just few episodes overcoming need imposing extra constraints as required by state-of-the-art methods, is still missing. I...

The dependence between assets tends to increase when the market declines. This paper develops a correlation measure focusing on market declines using the expected shortfall (ES), referred to as the ES-implied correlation, to improve the existing value at risk (VaR)-implied correlation. Simulations which define period-by-period true correlations show that the ESimplied correlation is much closer...

In this paper, we discuss a multiperiod portfolio selection problem with fuzzy returns. We present a new credibilitic multiperiod mean semi- absolute deviation portfolio selection with some real factors including transaction costs, borrowing constraints, entropy constraints, threshold constraints and risk control. In the proposed model, we quantify the investment return and risk associated with...

background: northern regions of iran have been encountered to dominate malignancies of gastrointestinal (gi) tract. we came to examine the total excess mortality due to the gi cancer in mazandaran province. methods: socio-demographic and clinical data of 484 patients with gi cancer collected during the years 1990-1991were available from babol cancer registry. patients were followed up for 15 ye...

Value at risk (VaR) is an important and widely used measure of the extent to which a given portfolio is subject to risk present in nancial markets. Considerable amount of research was dedicated during recent years to development of acceptable methods for evaluation of this risk measure. In this paper, we present a method of calculating the portfolio which gives the optimal VaR among those, whic...

In this chapter, we estimate the Expected Shortfall (ES) in conditional autoregressive expectile models by using a nonparametric multiple expectile regression via gradient tree boosting. This approach has the advantages generated by the flexibility of not having to rely on data assumptions and avoids the drawbacks and fragilities of a restrictive estimator such as Historical Simulation. We cons...

Bayesian inference about small areas is of considerable current interest, and simultaneous intervals for the parameters for the areas are needed because these parameters are correlated. This is not usually pursued because with many areas the problem becomes difficult. We describe a method for finding simultaneous credible intervals for a relatively large number of parameters, each corresponding...

The recent spate of theoretical models of behaviour under ambiguity can be partitioned into two sets: those involving multiple priors (in which the probabilities of the various events are not known but probabilities can be attached to the various possible values for the probabilities) and those not involving multiple priors. This paper concentrates on the first set and provides an experimental ...

Proceeding from theoretical hypotheses, driving forces for the introduction of the ISO 9000ff series in innovative service companies are analysed. Based on the European Community Innovation Survey (CIS), a probit model is estimated with different explanatory variables based on a sample of innovative German service companies. The first analysis of the role of the quality standard ISO 9000ff in G...