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of real estate prices. To meet these requirements, we develop and compare multilevel structured additive regression … models based on GAMLSS type approaches and quantile regression, respectively. Our models involve linear, nonlinear and …
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The increasing availability of geospatial data (i.e., exact longitudes and latitudes for each house) has the potential to improve the quality of house price indexes. It is not clear though how best to use this information. We show how geospatial data can be included as a nonparametric spline...
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in fact non-linear, misspecifying the functional form can adversely affect inference. While mean regression models only … semi-parametrically using Bayesian geoadditive quantile regression models. The flexibility of this model class overcomes … results stress the importance of making use of semi-parametric regression models as several covariates influence farmland …
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