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Auction Price Estimation for Used Cars by Regression
Methods
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Yusuke Soejima, Hideo Hirose
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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.
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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. |
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Weibull
model; truncated data; number of failures; SIR model; differential
equation, best-backward solution.
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@
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Cited in Books:
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