Adaptive models in regression for modeling and understanding evolving populations

Abstract : When regression analysis is carried out in a prediction purpose, an evolution in the modeled phenomenon between the training and the prediction stages obliges the practitioner to perform a new and complete analysis. Similarly, when regression aims to explain the modeled phenomenon, a new regression model must be estimated whenever the phenomenon or its study conditions change. This paper shows how a previous regression analysis can be used for the estimation of the regression model in a new situation avoiding a new and expensive collect of data. Two case studies are considered in the paper. On the one hand, a regression model of the house price versus house and household features is adapted from a city of the US South-East (Birmingham, AL) to a city of the US West coast (San Jose, CA). On the other hand, the link between CO$ emissions and gross national product in 1999 is analyzed based on a previous analysis dating from 1980.
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Journal of Case Studies in Business, Industry and Government Statistics (CSBIGS), 2011, 4 (2), pp.83-92
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Charles Bouveyron, Patrice Gaubert, Julien Jacques. Adaptive models in regression for modeling and understanding evolving populations. Journal of Case Studies in Business, Industry and Government Statistics (CSBIGS), 2011, 4 (2), pp.83-92. 〈hal-00517673〉

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