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Communication Dans Un Congrès Année : 2008

Adaptive linear models in regression for the modeling of housing market in different U.S. cities

Résumé

The general setting of regression analysis is to identify a relationship between a response variable $Y$ and one or several explanatory variables $\textbf{X}$ by using a learning sample. In a prediction framework, the main assumption for predicting $Y$ on a new sample of $\textbf{X}$ observations is that the regression model $Y=f(\textbf{X})+\epsilon$ is still valid. Unfortunately, this assumption is not always true in practice. We therefore propose to adapt the original regression model to the new sample by estimating a transformation between the original regression function $f(\textbf{X})$ and the new one $f^{*}(\textbf{X})$. The interest of this work is illustrated by an application to the modeling of housing market in different U.S. cities in which the regression model of a reference city is adapted to another city.
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Dates et versions

hal-00272874 , version 1 (12-04-2008)

Identifiants

  • HAL Id : hal-00272874 , version 1

Citer

Charles Bouveyron, Julien Jacques. Adaptive linear models in regression for the modeling of housing market in different U.S. cities. Computational Methods for Modelling and Learning in Social and Human Sciences (MASHS'08), Jun 2008, Créteil, France. ⟨hal-00272874⟩
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