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Hoang Nguyen

comparison experiment for gathering expert judgement for an aircraft wiring risk estimation. Reliability engineering & System safety 93, 2008

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Przemysław Busse

Fatality at Wind facilities in the U.S . NWCC Res. Meeting, Milwaukee, November. Fernley J. 2008. Birds, wind farms and collision modelling: a study of golden eagles . Unpublished Report, West Coast Energy Developments Ltd, Mold. Fernley J. 2009. A Review of “Collision Avoidance of Golden Eagles at Wind Farms under the Band Collision Risk Model” by P. Whitfield. Fernley J., Lowther S., Whitfield P. 2006. A review of goose collisions at operating wind farms and estimation of the goose avoidance rate . Rep. to West Coast

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Nevena Eremić and Mirjana Đerić

. Circulation 2002; 105: 310-15. Assman G, Cullen P, Shulte H. The Munster Heart Study (PROCAM): Results of follow-up at 8 years. Eur Heart J 1998; 19 (Suppl A): A2-A11. International Task Force for Prevention of Coronary Heart Disease. http://www.chd-taskforce.com Conroy RM, Pyörälä K, Fitzgerald AP, Sams S, Meniotti A, Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE Project. Eur Heart J 2003; 24: 987-1003. Erhardt LR

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Hoang Nguyen

4. References [1] Abdelgawad M., Fayek A.R.: Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP, J. Constr. Eng. Manage, 136 (9), 2010, 1028–1036. [2] Atanassov K.T.: Intuitionistic Fuzzy Sets, Fuzzy Sets and Systems, 20, 1986, 87-96. [3] Brandowski A., Frąckowiak W., Nguyen H., Podsiadło A.: Risk estimation of the seagoing ship casualty as the consequence of the propulsion loss, Proceedings of ESREL 2009 Conference, Taylor & Francis, Vol. 3, 2010, pp. 2345-2349. [4] Chen S.M., Tan J.M.: Handling

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Jiajun He, Huimin Zhang, Hui Zhang, Xuan Guo, Mingwei Song, Junhao Zhang and Xiaotao Li

. 1997;387(15):235-260. [21] Liu S, Dong X, Li Z, Li J. Construction and application of estimation model of economic loss from heavy metal pollution. Environ Protect Sci. 2010;36(3):81-84. [22] Delgado J, Barba-Brioso C, Nieto JM, Boski T. Speciation and ecological risk of toxic elements in estuarine sediments affected by multiple anthropogenic contributions (Guadiana saltmarshes, SW Iberian Peninsula): I. Surficial sediments. Sci Total Environ. 2011;409(19): 666-3679. [23] Varol M. Assessment of heavy metal contamination in sediments of the Tigris River (Turkey

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Innocent Sitima and Clifford K. Hlatywayo

References Barry, C. B., & Rodriguez, M. 2004. Risk and return characteristics of property indices in emerging markets. Emerging Markets Review 5: 131-159. Berger, T. 2013. Forecasting value-at-risk using time varying copulas and EVT return distributions. Journal of International Economics 133: 93-106. Chen, Q., & Chen R. 2013. Method of Value-at-Risk and empirical research for Shanghai stock market. Procedia Computer Science 17: 671-677. Cheng, G., Li P., & Shi, P. 2007. A new algorithm based on

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Vassil Sgurev and Stanislav Drangajov

Abstract

The innovation introduction, no matter whether a product, technology, a method, etc., is being implemented, is connected with considerable risk of investments loss and highly stochastic behaviour, depending on unpredictable factors. It is acknowledged that the innovation passes at least through 6 general stages: 1 - Prestart stage; 2 - Start stage; 3 - Initial expansion stage; 4 - Quick expansion stage; 5 - Stage of reaching liquidity of venture investments; 6 - Stage of project failure and its cancelling. Each state may be refined in details. It is important for the Decision Maker (DM) on one hand to be able to estimate the risk of transitions from a state to state and the probability also for profitable outcome of the initiative - for this purpose a network flow model is proposed; and on the other hand - it is useful if the DM may apply different actions at each stage so that to minimize the losses and maximize the final profit - for this purpose a Markov Decision Process is proposed, which is very closely related to the network flow model and both may be united in a Markov flow.

Open access

Gabriel Gaiduchevici

., Ibragimov, R., and Permiakova, E. (2010). Copula estimation. In Copula theory and its applications , pages 77–91. Springer. 5. Embrechts, P., McNeil, A., and Straumann, D. (2002). “Correlation and dependence in risk management: properties and pitfalls.” Risk management: value at risk and beyond, pages 176–223. 6. Fermanian, J.-D. and Wegkamp, M. H. (2012). “Time-dependent copulas.” Journal of Multivariate Analysis , 110:19–29. 7. Genest, C., Quessy, J.-F., and Remillard, B. (2006). “Goodness-of-fit procedures for copula models based on the probability

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Vesna Bucevska

References 1. Andjelić, G., Djaković, V., Radišić, S. (2010), “Application of VaR in Emerging markets: A Case of Selected Central and Eastern European Countries”, African Journal of Business Management, Vol. 4, No. 17, pp. 3666-3680. 2. Angabini, A., Wasiuzzaman, S. (2011), “GARCH Models and the Financial Crisis-A Study of the Malaysian Stock Market”, The International Journal of Applied Economics and Finance, Vol. 5, No. 3, pp. 226-236 . 3. Angelidis, T., Degiannakis, S. (2005), "Modeling Risk for

Open access

Wiesław Piwowarski, Zbigniew Isakow and Jacek Juzwa

. Piwowarski W., Isakow Z., Juzwa J., 2012. Analysis and risk estimation of hazards to environmental components in sub-areas of mining deformations of geological structures. Annual International Conference on Geological & Earth Sciences – GEOS (Proceeding 37), Singapore. Walley P., 1996. Measures of uncertainty in expert systems . Artificial Intelligence, Vol. 83. Wang P., 1994. A defect in Dempster-Shafer theory. Technical Report N. 85 of CRCC. Indiana, Indiana University. Whitlock R.R., McCaskill T.B., 2009. Development of the Global Position System