Title
Auction Price Estimation for Used Cars by Regression Methods

Authors

Yusuke Soejima, Hideo Hirose


Source

Joint Meeting of the Korea-Japan Conference of Computational Statistics and the 25th Symposium of Japanese Society of Computational Statistics, pp.9-12, November 11-12, 2011, Korea.


Abstract
In predicting the prices for auctions, we often use linear regression methods where the objective variable is the price. To find the estimate for price, we apply regularization methods in regression such as the ridge, lasso, and their relatives. In used car auctions, these methods provide very similar accuracy in the sense of the RMSE, the root-mean-squared error. However, we have found that the accuracy becomes higher when we use the k nearest-neighbor (k-NN) regression method with selected variables via the linear regression methods to this kind of auctions.

Key Words
Weibull model; truncated data; number of failures; SIR model; differential equation, best-backward solution.

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