First CLADAG data mining prize : data mining for longitudinal data with different marketing campaigns
Akacha, Mouna, Fonseca, Thaís C. O. and Liverani, Silvia (2009) First CLADAG data mining prize : data mining for longitudinal data with different marketing campaigns. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. (Working papers).
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The CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society recently organised a competition, the 'Young Researcher Data Mining Prize' sponsored by the SAS Institute. This paper was the winning entry and in it we detail our approach to the problem proposed and our results. The main methods used are linear regression, mixture models, Bayesian autoregressive and Bayesian dynamic models.
|Item Type:||Working or Discussion Paper (Working Paper)|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Faculty of Science > Statistics|
|Library of Congress Subject Headings (LCSH):||Data mining, Longitudinal method, Marketing -- Econometric models|
|Series Name:||Working papers|
|Publisher:||University of Warwick. Centre for Research in Statistical Methodology|
|Place of Publication:||Coventry|
|Number of Pages:||22|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
|Funder:||University of Warwick. Centre for Research in Statistical Methodology|
|References:||1. Belsley, D.A., E. Kuh, and R.E. Welsch (2004). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 1st ed., John Wiley & Sons, Inc. New York. 2. Benzecri, J. (1992). Correspondence analysis handbook, vol. 125 of Statistics: Textbooks and Monographs. 3. Chambers, J. M. (1992). Linear models. Chapter 4 of Statistical Models in S, eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole. 4. Chatfield, C. (2003). The Analysis of Time Series: an Introduction. CRC Pr I Llc. 5. Croux, C., Filzmoser, P. and Oliveira, M. (2007). Algorithms for Projection-Pursuit Robust Principal Component Analysis. Chemometr. Intell. Lab. , Vol. 87, pp. 218-225. 6. Gamerman, D. and Lopes, H.F. (2006). Markov chain Monte Carlo: stochastic simulation for Bayesian inference. Chapman & Hall/CRC. 7. Gneiting, T. and Raftery, A. E. (2007). Strictly proper scoring rules, prediction and estimation. Journal of the American Statistical Association, 102(477): pp.360–378. 8. Hastie, T. J. and Pregibon, D. (1992). Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole. 9. O’Brien, R. M. (2007). A Caution Regarding Rules of Thumb for Variance Inflation Factors. Quality and Quantity 41(5):673–690. 10. Pole, A. andWest, M. and Harrison, J. (1994). Applied Bayesian forecasting and times series analysis. Chapman & Hall/CRC.|
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