Elena Dalla Chiara, Martina Menon and Federico Perali
.” Fiscal Studies 16(3): 40–54. Doi: https://doi.org/10.1111/j.1475-5890.1995.tb00226.x .
Brandolini, A., S. Magri, and T. Smeeding. 2010. “Asset-based Measurement of Poverty.” Journal of Policy Analysis and Management 29(2): 267–284. Doi: https://doi.org/10.1002/pam.20491 .
Brewer, M. and C. O’Dea. 2012. Measuring Living Standards with Income and Consumption: Evidence from the UK . Institute for Social and Economic Research, University of Essex and Institute for Fiscal Studies (Working Paper n. 2012-05). Available at: https
Baiba Ieviņa, Nils Rostoks, Naeem H. Syed, Andrew J. Flavell and Gederts Ievinsh
coastal dunes. In: Environmental Engineering. Proceedings of the 7th International Conference, May 22–28, 2008 . Vilnius Gediminas Technical University, pp. 22–28.
Bonin, A., Nicole, F., Pompanon, F., Miaud, C., Taberlet, P. (2007). Population adaptive index: A new method to help measure intraspecific genetic diversity and prioritize populations for conservation. Conserv. Biol ., 21 , 697–708.
de Bruin, A., Ibelings, B. W., Van Donk, E. (2003). Molecular techniques in phytoplankton research: From allozyme electrophoresis to genomics. Hydrobiologia , 491
Li-Chun Zhang, Ingvild Johansen and Ragnhild Nygaard
Official Statistics 12: 199–222. Available at: https://search.proquest.com/docview/1266834049?pq-origsite=gscholar (accessed June 2019).
Balk, B.M. 2001. Aggregation Methods in International Comparisons: What Have We Learned? ERIM Report, Erasmus Research Institute of Management, Erasmus University Rotterdam. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=370897 (accessed June 2019).
Chessa, A.G. 2016. “A New Methodology for Processing Scanner Data in the Dutch CPI.” Eurostat review of National Accounts and Macroeconomic Indicators
://www.bancaditalia.it/statistiche/raccolta-dati/sistema-informativo-statistico/modellazione/matrixmod.pdf (accessed May 2015).
Bergamasco, S., A. Cardacino, F. Rizzo, M. Scanu, and L. Vignola. 2013. “A Strategy on Structural Metadata Management Based on SDMX and the GSIM Models.” Work Session on Statistical Metadata (METIS), Geneva, 6-8 May 2013. Available at: http://www.unece.org/stats/documents/2013.05.metis.html#/ (accessed April 2015).
Brancato, G., C. Pellegrini, M. Signore, and G. Simeoni. 2004. “Standardising, Evaluating and Documenting Quality: the Implementation of Istat Information System for Survey Documentation - SIDI.” In
Baozhen Yao, Ping Hu, Mingheng Zhang and Maoqing Jin
Automated Incident Detection (AID) is an important part of Advanced Trafﬁc Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the inﬂuence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artiﬁcial Neural Networks (ANNs) in freeway incident detection.
Paul Biemer, Dennis Trewin, Heather Bergdahl and Lilli Japec
Biemer, P.P. 2010. “Total survey error design, implementation, and evaluation.” Public Opinion Quarterly 74: 817-848. DOI: http://dx.doi.org/10.1093/poq/nfq058.
Deming W. Edwards. (n.d.). BrainyQuote.com. Available at: http://www.brainyquote.com/quotes/quotes/w/wedwardsd380788.html (accessed July 28, 2014).
Office of Management and Budget 2001. “Measuring and Reporting Sources of Error in Surveys”. Statistical Policy Office, Working Paper 31. Available at: https
Computational Mathematics 24(1-4): 311-331.
Fukuda, T., Morimoto, Y., Morishita, S. and Tokuyama, T. (1996). Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization, Proceedings of the 1996 ACM-SIGMOD International Conference on the Management of Data, Montreal, Quebec, Canada, pp. 13-23.
Geng, L. and Hamilton, H. (2006). Interestingness measures for data mining: A survey, ACM Computing Surveys 38(3), Article no. 9.
Greco, S., Pawlak, Z. and Słowi´nski, R. (2004). Can Bayesian
-comparison of the effects on ranking of IR systems, Information Processing & Management 41(5): 1019-1033.
Liu, T.-Y. (2009). Learning to rank for information retrieval, Foundations and Trends in Information Retrieval 3(3): 225-331.
Nieddu, L. and Rizzi, A. (2007). Proximity measures in symbolic data analysis, Statistica 63(2): 195-211.
Pecina, P. (2005). An extensive empirical study of collocation extraction methods, Proceedings of the Association for Computational Linguistics Student Research Workshop, Ann Arbor, MI, USA, pp
.H.—HWANG, M.K.: A framework for measuring the performance of service supply chain management , Computers & Industrial Engineering 62 (2012), no. 3, 801–818; https://www.sciencedirect.com/science/article/pii/S0360835211003378
 DOU, Y.—ZHU, Q.—SARKIS, J.: Evaluating green supplier development programs with a grey-analytical network process-based methodology , European Journal of Operational Research 233 (2013), no. 2, 420–431; https://www.sciencedirect.com/science/article/pii/S0377221713002129
 DRABIKOVÁ,E.: Examination of the Company Valuation
measures for geometric Levy processes, Finance and Stochastics 7 (1): 509-531.
Glasserman, P. (2004). Monte Carlo Methods in Financial Engineering , Springer-Verlag, New York, NY.
Hull, J.C. (1997). Options, Futures and Other Derivatives , Prentice Hall, Upper Saddle River, NJ.
Jacod, J. and Shiryaev, A. (1987). Limit Theorems for Stochastic Processes , Springer-Verlag, Berlin/Heidelberg/New York, NY.
Kou, S.G. (2002). A jump-diffusion model for option pricing, Management Science 48 (8): 1086