The nature of statistical learning theory
The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Semiparametric support vector and linear programming machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Geoadditive expectile regression
Computational Statistics & Data Analysis
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Semiparametric regression models have been extensively used to predict housing sales prices, but semiparametric kernel machines with spatial effect have not been studied yet. This paper proposes the semiparametric spatial effect kernel minimum squared error model (SSEKMSEM) and the semiparametric spatial effect least squares support vector machine (SSELS-SVM) for estimating a hedonic price function and compares the price prediction performance with the conventional parametric models and a semiparametric generalized additive model (GAM). This paper utilizes two data sets. One is a large data set representing 5966 single-family residential home sales between July 2000 and August 2008 from Pitt County, North Carolina. The other is a data set of residential property sales records from September 2000 to September 2004 in Carteret County, North Carolina. The results show that the SSEKMSEM and SSELS-SVM outperform the parametric counterparts and the semiparametric GAM in both in-sample and out-of-sample price predictions, indicating that these kernel machines can be useful for measurement and prediction of housing sales prices.