ENBIS-13 in Ankara15 – 19 September 2013 Abstract submission: 5 February – 5 June 2013
The following abstracts have been accepted for this event:
Risk Assessment of Turkish External Debt by Using Hidden Markov Model
Authors: Ceren Eda Can (Hacettepe University), Gül Ergün (Hacettepe University)
Primary area of focus / application: Finance
Keywords: Hidden Markov model, Normal distribution, Turkish external debt, External debt management, Borrowing limit, Country risk
Submitted at 15-Apr-2013 10:45 by Ceren Eda Can
In particular, economic crisis in Turkey is mostly triggered by its external debt. Therefore, the external debt is the one of the most importand problems in Turkey. In the scope of Turkish debt management system, it is a crucial task to model the structure of Turkish external debt and then define both its borrowing limits and the country risk, which represents the credibility of the Turkey on international financial markets and represents the level for the sustainability of the debt from Turkey.
The aim of this study is to model the return of total Turkish external debt stocks and some indicators of Turkish external debt by Hidden Markov Model (HMM). The financial time series has a dynamic structure and thus irrational rapid changes in the series can be a part of a pattern. Each pattern consists of different regime, which represents the economic situation. The long-term trend and short-term sideway movements can be defined by the change of regime. When modeling the financial time series, HMM allows for not only the change of regime but also the dependency between regimes. In this study, Normal distribution is chosen for the impacts of different regimes and then Normal-HMM is used to forecast the future level of Turkish external debt and also determine its structure in order to take necessary policies, which ensure economic stabilitiy.
Treatment of a Functional Input for the Optimization of a Computer-based Model
Authors: Miguel Munoz Zuniga (IRSN), Yann Richet (IRSN), Gregory Caplin (IRSN)
Primary area of focus / application: Design and analysis of experiments
Keywords: Functional data, Dimension reduction, Functional optimization, Computer experiments, Kriging
Submitted at 15-Apr-2013 11:13 by Miguel Munoz Zuniga
One of the main IRSN concern is the risk measurement all along the nuclear fuel cycle. The so called ”burnup” problematic is to consider an irradiated nuclear fuel assembly after its life in the core. One assembly can be characterized by its rate of combustion along its vertical axis (taken as a functional data), which might lead to an uncontrolled neutronic chain reaction. Hence, the ”burn-up” problem consists in finding the most penalizing ”burn-up” profile regarding the neutronic criticality coefficient all along the storage.
In order to measure the storage criticality risk, a simulation chain model is used, starting from the depletion of the fuel, ending with the modeling of its neutronic behaviour. These two models are computed through the HPC platform PROMOTHÉE, driven by R. The algorithm presented involves functional data treatment and approximation, dimension reduction, Monte Carlo sampling, optimization and efficiently solves this functional optimization problem. Beyond these mathematical tools, we will also focus on the technical issues derived from launching the underlying mechanistic simulations, which are mostly not related to R, high CPU consuming and available through a remote computing cluster.
Bayesian Analysis and Prediction of Patients' Demands for Visits in Home Health Care
Authors: Raffaele Argiento (CNR-IMATI), Alessandra Guglielmi (Dipartimento di Matematica, Politecnico di Milano), Ettore Lanzarone (CNR-IMATI), Inad Nawajah (Dipartimento di Matematica, Politecnico di Milano)
Primary area of focus / application: Other: ISBA/IS Invited Session
Keywords: Home care, Bayesian modeling and estimation, MCMC algorithm, Random effect
Submitted at 15-Apr-2013 11:46 by Raffaele Argiento
In the literature, several studies deal with stochastic models for representing patient conditions in the health care system but, to the best of our knowledge, few works deal with HC service and furthermore Bayesian approaches have not been considered in the HC context, yet.
The aim of this talk is to propose a methodology for estimating and predicting the demand for care by HC patients in terms of number of visits N required in a defined time slot. Patients are characterized by a Care Profile (CP) which varies along
with the time secondary to a periodic revision or sudden variations in
health state. Our approach considers the joint distribution of N and CP over time as a
conditional distribution of N given CP, times the marginal of the CPs; in addition, the transition between CP states is regulated by a homogeneous multistate Markov Chain. The proposed model is developed and validated considering the data of
one of the largest HC providers in Italy. We obtain the posterior densities of model parameters through MCMC simulation and predict the number of visits of patients
in future time slots. Results show the applicability of the approach in the practice
and a good predictive fit of the model to the data.
On-line Process Monitoring Using Partial Correlations
Authors: Tiago Rato (University of Coimbra), Marco Reis (University of Coimbra)
Primary area of focus / application: Process
Keywords: Process monitoring, Multivariate dynamical processes, Variable transformation, Partial correlations, Marginal correlations, Individual observations
Submitted at 15-Apr-2013 14:06 by Tiago Rato
Accepted (view paper)
The proposed methodologies were applied on multivariate systems and their performances were compared against current alternatives. The results obtained showed that the sensitivity enhancing transformations play a major role on the monitoring statistics performance, allowing them to detect faults more rapidly than with original variables.
A New High Order Fuzzy Time Series Method Utilizing on Fuzzy Rule Based Systems
Authors: Murat Alper Başaran (Akdeniz University Faculty of Engineering at Alanya)
Primary area of focus / application: Modelling
Keywords: Fuzzy set, fuzzy rule based systems, high order fuzzy time series, forecasting, fuzzy numbers
Submitted at 15-Apr-2013 16:03 by Murat Alper Başaran
Grading of Carcasses - How to Measure Lean Meat Percentage?
Authors: Froydis Bjerke (Animalia Meat and Poultry Research Centre)
Primary area of focus / application: Other: applied regression analysis
Keywords: Multiple linear regression, Data analysis, Carcass grading, Applied statistics
Submitted at 15-Apr-2013 16:31 by Froydis Bjerke
Accepted (view paper)
The presentation is a case study of applied regression analysis where data collection is expensive and tedious, and the economical impact is huge throughout the meat production value chain.
The objective of the study is to build a model for monitoring carcass grading from regularly collected meat cutting data. The model utilises LM% estimated from CT scans (images) and corresponding meat cutting data from 144 carcasses. Model properties and sources of variance are also discussed.