The role of coherence for the integration of different sources

4th International Symposium on Imprecise Probabilities and Their Applications, ISIPTA 2005

Volumn , Issue , 2005, Pages

  Vantaggi, Barbara ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

Coherent conditional probability; Data fusion; Inference; Statistical matching

Indexed keywords

DATA FUSION; MARKETING;

COHERENT CONDITIONAL PROBABILITY; CONDITIONAL INDEPENDENCES; INFERENCE; INTEGRATION PROBLEMS; LOGICAL CONSTRAINTS; MICROSIMULATION MODELS; PROBABILISTIC ASSESSMENTS; STATISTICAL MATCHING;

PROBABILITY;

EID: 84947091670     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (36)

1. M. Baioletti, A. Capotorti, S. Tulipani and B. Vantaggi. Elimination of Boolean variables for probabilistic coherence. Soft Computing, 4(2): 81-88, 2000.
2. V. Biazzo and A. Gilio. On the linear structure of betting criterion and the checking of coherence. Annals of Mathematics and Artificial Intelligence, 35: 83–106, 2002.
3. E.C. Budd. The creation of a microdata file for estimating the distribution of income. Review of Income and Wealth, 17: 317–333, 1971.
4. A. Capotorti and B. Vantaggi. Locally strong coherence in inferential processes. Annals of Mathematics and Artificial Intelligence, 35: 125–149, 2002.
5. A. Capotorti, L. Galli and B. Vantaggi. Locally strong coherence and inference with lower-upper probabilities. Soft Computing, 7(5): 280–287, 2003.
6. G. Coletti. Coherence principles for handling qualitative and quantitative partial probabilistic assessments. Mathware & Soft Computing, 3: 159-172, 1994.
7. G. Coletti and R. Scozzafava. Exploiting zero probabilities. Proc. of EUFIT97 (ELITE Foundation, Aachen, Germany) 1499-1503, 1997.
8. G. Coletti and R. Scozzafava. Probabilistic logic in a coherent setting. Trends in logic n.15, Dordrecht/Boston/London: Kluwer, 2002.
9. M. D’Orazio, M. Di Zio and M. Scanu. Statistical matching and official statistics. Rivita di Statistica Ufficiale, 1/2002: 5–24, 2002.
10. M. D’Orazio, M. Di Zio and M. Scanu. Statistical matching and the likelihood principle:uncertainty and logical constraints. Technical Report Contributi 2004/1, Italian Statistical Ististute, Roma.
11. G. De Cooman, and M. Zaffalon. Updating beliefs with incomplete observations. Artificial Intelligence, 159: 75-125, 2004.
12. B. de Finetti. La prévision: ses lois logiques, ses sources subjectives. Annales de l’Institut Henri Poincaré, 7: 1–68, 1937.
13. B. de Finetti. Teoria della probabilitá. Torino: Einaudi, 1970 (Engl. Transl. (1974) Theory of probability, London: Wiley & Sons).
14. L.E. Dubins. Finitely additive conditional probabilities, conglomerability and disintegration. Annals of Probability 3: 89–99, 1975.
15. C. Genest and J. V. Zidek. Combining probability distributions: a critique and an annotated bibliography. Statistical Science, 1(1): 114–148, 1986.
16. B. Jaumard, P. Hansen and M. Poggi de Aragão. Column generation methods for probabilistic logic. ORSA Journal on Computing, 3: 135–148, 1991.
17. P. Hansen, B. Jaumard and M. Poggi de Aragão. Mixed-integer column generation algorithms and the probabilistic maximum satistability problem. European Journal of Operational Research, 108: 671–683, 1998.
18. J. B. Kadane. Some statistical problems in merging data files. Journal of Official Statistics, 17: 423–433, 2001.
19. W. A. Kamakura and M. Wedel. Statistical data fusion for cross-tabulation. Journal of Marketing Research, 34: 485–498, 1997.
20. C.F. Manski. Identification problems in the social sciences. Cambridge, MA: Harvard University Press, 1995.
21. B.A. Okner. Constructing a new data base from existing microdata sets: the 1966 merge file. Annals of Economics and Social Measurement, 1(3): 325–342, 1972.
22. B. A. Okner. Data matching and merging: an overview. Annals of Economic and Social Measurement, 3(2): 347–352, 1974.
23. G. Paass. Statistical match: evaluation of existing procedures and improvements by using additional information. In Microanalytic Simulation Models to Support Social and Financial Policy, (Eds. G.H. Orcutt and H. Quinke) Elsevier Science, Amsterdam p. 401–422, 1986.
24. S. Rässler. Statistical matching: a frequentist theory, practical applications and alternative Bayesian approaches. Lecture Notes in Statistics, Springer Verlag, 2002.
25. R. H. Renssen. Use of statistical matching techniques in calibration estimation. Survey Methodology, 24: 171–183, 1998.
26. W. L. Rodgers. An evaluation of statistical matching. Journal of Business and Economic Statistics, 2(1): 91–102, 1984.
27. D.B. Rubin. Characterizing the estimation of parameters in incomplete-data problems. Journal of the American StatisticalAssociation, 69: 467-474, 1974.
28. D.B. Rubin. Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business & Economic Statistics, 2(1): 87–94, 1986.
29. C. A. Sims. Comments (on Okner). Annals of Economic and Social Measurement, 1(3): 343–345, 1972.
30. A. C. Singh, H. J.Mantel, M. D. Kinack and G. Rowe. Statistical matching: use of auxiliary information as an alternative to conditional independence assumption. Survey Methodology, 19(1): 59–79, 1993.
31. P. Szivós, T. Rudas, I.G. Tóth. A tax-benefit microsimulation model for Hungary. Workshop on Microsimulation in the New Millennium: Challenges and Innovations, Cambridge, 1998.
32. J.L. Schafer. Analysis of incomplete multivariate data. Chapman & Hall, London, 1997.
33. B. Vantaggi. The role of coherence for handling probabilistic evaluations and independence. Soft Computing, 2005 (published online october 2004).
34. M. Wolfson, S. Gribble, M. Bordt, B. Murphy and G. Rowe. The social policy simulation database and model: an example of survey and administrative data integration. Survey of Current Business, 69: 36–41, 1989.
35. P. Walley. Statistical Reasoning with Imprecise Probabilities. London: Chapman & Hall, 1991.
36. M. Zaffalon. The naive credal classifier. Journal of Statistical Planning and Inference, 105: 5-21, 2002.