The Data Fusion Bibliography

THE DATA FUSION BIBLIOGRAPHY

(compiled by Roland Soong, updated as of 6/10/2006)

Abello, R. and Phillips, B. (2004) Statistical matching of the HES and NHS: an exploration of issues in the use of unconstrained and constrained approaches in creating a basefile for a microsimulation model of the pharmaceutical benefits segment. Technical report, Australian Bureau of Statistics. Methodology Advisory Committee Paper, June.

Achen, C.H. and Shively, W.P. (1995) Cross-Level Inference. University of Chicago Press: Chicago, IL.

Adamek, J. (1994) Fusion: combining data from separate sources. Marketing Research: A Magazine of Management and Applications, 6, 48-50.

Aerts, M., Claeskens, G., Hens, N. and Molenberghs (2002) Local multiple imputation. Biometrika, 89, 375-388.

Aglietta, J. (2003) La France Plurimedia. Lessons drawn from a fusion between surveys and a clustering. ARF/ESOMAR Week of Audience Measurement, Los Angeles, California, USA, June 18, 2003.

Ahmed, S. and Lachenbruch, P.A. (1983) Discriminant analysis when scale contamination is present in the initial sample. In Classification and Clustering. (J. van Ryzin (ed.)). New York: Academic Press.

Ahuja, R.K., Magnanti, T.L. and Orlin, J.B. (1993) Network Flows: Theory, Algorithms and Applications. New York: Prentice Hall.

Aitchison, J. and Aitkin, C.G.G. (1976) Multivariate binary discrimination by the kernel method. Biometrika, 63, 413-420.

Aitchison, J., Habbema, J.D.F. and Kay, J.W. (1977) A critical comparison of two methods of statistical discrimination. Applied Statistics, 26, 15-25.

Aitchison, J. and Silvey, S.D. (1958) Maximum likelihood estimation of parameters subject to restraints. Annals of Mathematical Statistics, 29, 813-828.

Akaike, H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723.

Alegre, J., Arcarons, J., Calonge, S. and Manresa, A. (2000) Statistical matching between datasets: An application to the Spanish Household Survey (EPF90) and the Income Tax File (IRPF90). In The Workshop on Fighting Poverty and Inequality Through Tax Benefit Reform: Empirical Approaches. Barcelona.

Allison, P. (2000) Multiple imputation for missing data: A cautionary tale. Sociological Methods and Research, 28 (3), 301-309.

Allison, P.D. (2001) Missing Data. Sage: Thousand Oaks, CA.

Alós, J.S. and Nafria, E. (1997) AUDE integral information analysis derived from consumer panels and audience panels. Marketing and Research Today, 25(2), 106-114.

Alter, H. E. (1974) Creation of a synthetic data set by linking records of the Canadian Survey of Consumer Finances with the Family Expenditure Survey 1970. Annals of Economic and Social Measurement, 2, 373-394.

Althauser, R. and Rubin, D.B. (1971) The computerized construction of a matched sample. American Journal of Sociology, 76, 325-346.

Aluja, T., Nonell, R., Rius, R., and Martínez, M. (1995) File grafting. In F. Mola and A. Morineau (eds) Actes du IIIème Congrès International d'Analyses Multidimensionnelles des Données - NGUS'95, Centre Int. de Statistique et d'Informatique Appliquées, CISIA-CERESTA, 23-32.

Aluja-Banet, T. and Thio, S. (2001) Survey data fusion. Bulletin of Sociological Methodology, 72, 20-36.

Alvey, W. and Jamerson, B. (eds.) (1997) Record Linkage Techniques – 1997. (Proceedings of an International Record Linkage Workshop and Exposition on March 20-21, 1997 in Arlington, Virginia) Washington, DC: Federal Committee on Statistical Methodology.

Alvord, C.H. III (1983) The pros and cons of goal programming: a reply. Computers and Operations Research, 10, 61-62.

Amemiya, T. (1984) Tobit models: a survey. Journal of Econometrics, 24, 3-61.

Anagnoson, J.T. (2000) Microsimulation of public policy. In Handbook of Public Information Systems (G.D. Garson (ed.)). New York: Marcel Dekker.

Anderson, J.A. (1969) Discrimination between k populations with constraints on the probabilities of misclassification. Journal of the Royal Statistical Society, B31, 123-139.

Anderson, J. (1972) Separate sample logistic discrimination. Biometrika, 59, 19-36.

Anderson, T.W. (1951) Classification by multivariate analysis. Psychometrika, 16, 31-50.

Anderston, T.W. (1973) Asymptotic evaluation of the probabilities of misclassification. In Discriminant Analysis and Applications (T. Cacoullos (ed)), 17-36. New York: Academic Press.

Anderson, T.W. and Bahadur, R.R. (1962) Classification into two multivariate normal distributions with different covariance matrices. Annals of Mathematical Statistics, 33, 420-431.

Antoine, J. (1985) A case study illustrating the objectives and perspectives of fusion techniques. Proceedings of the Salzburg Readership Symposium.

Antoine, J. and G. Santini (1987) Fusion techniques: alternative to single-source methods. European Research, 15 (August), 178-187.

Armstrong, J. (1989) An evaluation of statistical matching methods. Working Paper no. BSMD 90-003E, Methodology Branch, Statistics Canada, Ottawa.

Arnold, B.C., Castillo, E. and Sarabia, J.M. (1999) Conditional Specification of Statistical Models. New York: Spinger-Verlag New York.

Arnold, B.C., Castillo, E. and Sarabia, J.M. (2001) Conditionally specified distributions: An Introduction. Statistical Science, 16, 249-274.

Arnold, B.C. and Gokhale, D.V. (1994) On uniform marginal representation of contingency tables. Statistics and Probability Letters, 21, 311-316.

Arsham H (1989) A simplex type algorithm for general transportation problem: An alternative to stepping stone. Journal of the Operational Research Society, 40, 581-590.

Arsham H. (1992) Postoptimality analyses of the transportation problem. Journal of the Operational Research Society, 43, 121-139.

Arthanari, T.S. and Dodge, Y. (1981) Mathematical Programming in Statistics. New York: John Wiley & Sons.

Arthur, J.L. and Ravindran, A. (1978) An efficient goal programming algorithm using constraint partitioning and variable elimination. Management Science, 24, 867-868.

Assael, H. and Cannon, H. (1979) Do demographics help in media selection? Journal of Advertising Research, 19(6), 7-11.

Assael, H. and Poltrack, D.F. (1991) Using single-source data to select TV programs based on purchasing behavior. Journal of Advertising Research, 31(4), 9-17.

Assael, H. and Poltrack, D.F. (1993) Using single-source data to select TV programs based on purchasing behavior. Part II. Journal of Advertising Research, 33(1), 48-56.

Assael, H. and Poltrack, D.F. (1994) Can demographic profiles of heavy users serve as a surrogate for purchase behavior in selecting TV programs? Journal of Advertising Research, 34(1), 11-17.

Assael, H. and Poltrack, D.F. (1996) Single vs. double source data for TV program selection. Journal of Advertising Research, 36(6), 73-81.

Assael, H. and Poltrack, D.F. (1999) Relating products to TV program clusters. Journal of Advertising Research, 39(2), 41-52.

Assael, H. and Poltrack, D.F. (2002) Consumer surveys vs. electronic measures for single-sources data. Journal of Advertising Research, 42(5), 19-25.

Atrostic, B. K. (1994) A multiple imputation approach to microsimulation. Proceedings of the Survey Research Methods Section, American Statistical Association, 529-534. Washington, DC: American Statistical Association.

Bäck, T., Fogel, D.B. and Michalewicz, Z. (eds.) (1997) Handbook of Evolutionary Computation. New York: Oxford University Press.

Bäck, T. and Schwefel, H.P. (1993) An overview of evolutionary algorithms for parameters optimization. Evolutionary Computation, 1(1), 1-24.

Bajgier, S.M. and Hill, A.V. (1982) An experimental comparison of statistical and linear programming approaches to the discriminant problems. Decision Sciences, 13, 604-618.

Bailar, B. A. and Bailar, J. C. (1978) Comparison of two procedures for imputing missing survey values. Proceedings of the Section on Survey Research Methods, American Statistical Association, 462-467. Washington, DC: American Statistical Association.

Bailey, J. and Boller, S. (2001) Data fusion from the media sellers’ perspective. 10th Worldwide Readership Research Symposium, Venice, 119-127.

Baim, J. and Frankel, M.R. (1997) Enhanced ascription. Proceedings of the Magazine Audience Measurement Research. New York: Advertising Research Foundation.

Baker, K. (1990) The BARB/TGI Fusion. Technical Report on the Fusion Conducted in February/March 1990. Ickenham, UK: Ken Baker Associates.

Baker, K. (1996) What do we know about … data fusion? Admap, July.

Baker, K., Harris, P. and O’Brien, J. (1989) Data fusion: an appraisal and experimental evaluation. Journal of the Market Research Society, 31(2), 153-212.

Baker, K. Harris, P. and O’Brien, J. (1990) Data fusion: can perfection be reached? Journal of the Market Research Society, 32(3), 473-474.

Bakker, B.F.M. and Winkels, J.W. (1998) Why integration of household surveys? – Why POLS? Netherlands Official Statistics, 13, 5.7.

Balakrishnan, N. (1990) Modified Vogel's approximation method for the unbalanced transportation problem. Applied Mathematics Letters, 3(2), 9-11.

Balas, E. (1964) Extension de l’algorithme additif à la programmation en nombres entiers et à programmation nonlinéare. Comptes Rendues de l’Academie des Sciences, 258, 5136-5139.

Balinski, M.L. (1986) A competitive simplex method for the assignment method. Mathematical Programming, 34, 125-141.

Balinski, M.L. and Gomory, R.E. (1964) A primal method for the assignment and transportation problems. Management Science, 10, 578-593.

Ballas, D., Clarke, G. and Turton, I. (1999) Exploring microsimulation methodologies for the estimation of household attributes. Paper presented at the Fourth International Conference on Geo-Computation, Mary Washington College, Virginia, USA, July 25-28.

Bals, W. (2002) Controlled split survey in media practice. IMPUTE: Symposium on Nonresponse, Questionnaire Split and Multiple Imputation, Nuremberg, Germany, September 25.

Bandyopadhyay, S., Murphy, C.A. and Pal, S.K. (1995) Pattern classification using genetic algorithms. Pattern Recognition Letters, 16, 801-808.

Baniel, L. and Monistere, D. (2001) Single source data – qualitative and ratings data combined. Paper presented at ARF Week of Workshops, Chicago, IL.

Bar-Hen, A. and Daudin, J.-J. (1995) Generalization of the Mahalanobis distance in the mixed case. Journal of the Multivariate Analysis, 53, 332-342.

Barnard, J. and Meng, X.L. (1999) Application of multiple imputation in medical studies: from AIDS to NHANES. Statistical Methods in Medical Research, 8, 17-36.

Barnard, J. and Rubin, D.B. (1999) Small sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955.

Baron, R. (2001) A new practical approach to data fusion. Paper presented at ARF Week of Workshops, Chicago, IL.

Barr, R.S., Hickman, B.L. and Turner, J.S. (1994) Optimal microdatabase merging: a new method and applications. The Impact of Emerging Technology On Computer Science and Operations Research: ORSA CSTS Conference, Williamsburg, Virginia, January 5-7, 1994.

Barr, R., Hickman, B. and Turner, J.S. (2000) Optimal microdata file merging: a new model and network optimization algorithm. Presentation at the 2000 INFORMS National Conference, San Antonio, TX.

Barr, R.S., Stewart, W.H. and Turner, J.S. (1982) An empirical evaluation of statistical matching methodologies. Mimeograph, Edwin L. Cox School of Business, Southern Methodist University, Dallas, Texas.

Barr, R. S. and Turner, J. S. (1978) A new, linear programming approach to microdata file merging. In 1978 Compendium of Tax Research. Washington, DC: Office of Tax Analysis, U.S. Department of the Treasury, 131-149.

Barr, R.S. and Turner, J.S. (1981) Microdata file merging through large-scale network technology. Mathematical Programming Study, 15, 1-22

Barr, R.S. and Turner, J.S. (1990) Quality issues and evidence in statistical file merging. In Data Quality Control: Theory and Pragmatics (G.E. Liepins and V.R.R. Uppuluri (eds)), 245-313. New York: Marcel Dekker.

Barry, J.T. (1988) An investigation of statistical matching. Journal of Applied Statistics, 15, 275-283.

Bartlett, M.S. (1939) The standard errors of discriminant function coefficients. Journal of the Royal Statistical Society, Supplement, 6, 169-173.

Bartlett, M.S. (1951) The goodness of fit of a single hypothetical discriminant function in the case of several groups. Annals of Eugenics, 16, 199-214.

Bartlett, M.S. and Please, N.W. (1983) Discrimination in the case of zero mean differences. Biometrika, 50, 17-21.

Barlett, S., Krewski, D., Wang, Y. and Zielinksi, J.M. (1993) Evaluation of error rates in large scale computerized record linkage studies. Survey Methodology, 19(1), 3-12.

Bass, F.M. and Lonsdale, R.T. (1966). An exploration of linear programming in media selection. Journal of Marketing Research, 3, 179-188.

Bass, F.M., Tigert, D.J. and Lonsdale, R.T. (1968) Market segmentation: group versus individual behavior. Journal of Marketing Research, 5(3), 264-270.

Bauer, E. and Kohavi, R. (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning, 36, 105-139.

Baynton, P. (2003) Multi-media analysis. What is needed? ARF 49th Annual Convention and Research Infoplex, New York City, USA, April 2003.

Baynton, P. (2003) Data integration or fusion. Which is best for mixed media schedule analysis? ARF/ESOMAR Week of Audience Measurement, Los Angeles, California, USA, June 18, 2003.

Beal, G. (1945) Approximate methods in calculating discriminant functions. Psychometrika, 10, 205-218.

Beale, E.M.L., Hughes, P.A.B. and Broadbent, S.R.M. (1966). A computer assessment of media schedules. Operational Research Quarterly, 17, 381-411.

Beale, M.E. (1955) Cycling in the dual simplex algorithm. Naval Research Logistics Quarterly, 2(4), 269-275.

Beasley, J.E. (1997) A genetic algorithm for the generalised assignment problem. Computers and Operations Research, 23, 17-23.

Bedrick, E.J., Lapidus, J. and Powell, J.F. (2000) Estimating the Mahalanobis distance from mixed continuous and discrete data. Biometrics, 56, 394-401.

Bedwell, R. (1991) Fusion – Britain’s latest experience. In Fifth Worldwide Readership Symposium, Hong Kong.

Belin, T.R. (1993) Evaluation of sources of variation in record linkage through a factorial experiment. Survey Methodology, 19, 13-29.

Belin, T.R. and Rubin, D.B. (1990) Calibration of errors in computer matching for Census undercount (with discussion) Proceedings of the Government Statistics Section of the American Statistical Association, 124-131. . Washington, DC: American Statistical Association.

Belin, T.R. and Rubin, D.B. (1995) A method for calibrating false-match rates in record linkage. Journal of the American Statistical Association, 90, 694-707.

Bello, A. (1993). Choosing among imputation techniques for incomplete multivariate data: a simulation study. Communications in Statistics - Theory and Methods, 22, 853-877.

Bello, A.L. (1995) Imputation techniques in regression analysis: Looking closely at their implementation. Computational Statistics and Data Analysis, 20, 45-57.

Bennett, K.P. (1999) Combining support vector and mathematical programming methods for classification. In Schölkopf, B., Burges, C. and Smola, A. (eds) Advances in Kernel Methods – Support Vector Machines, p. 307-326. MIT Press: Cambridge, MA.

Bennett, K.P. and Mangasarian, O.L. (1992) Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1, 23-34.

Bennike, S (1985) Fusion --- an overview by an outside observer. Proceedings of the Salzburg Readership Symposium.

Bergg. D., Kite, J. and Silman, R. (1992) TV-AM Target Group Ratings: How TGR allowed TV-AM to make their clients an offer they couldn’t refuse. In Research Works – Papers from the AMSO Reseach Effectiveness Awards, 1991 (D. Martin and J. Goodyear (eds)), 118-137. NTC Publications Ltd.

Bergsma, W.P. and Rudas, T. (2002) Marginal models for categorical data. Annals of Statistics, 30, 140-159.

Bergstrahl, E.J., Kosanke, J.L. and Jacobsen, S.L. (1996) Software for optimal matching in observational studies. Epidemiology, 7, 331-332.