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New Calibration Methods with Asymmetric Data
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 Title & Authors
New Calibration Methods with Asymmetric Data
Kim, Sung-Su;
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 Abstract
In this paper, two new inverse regression methods are introduced. One is a distance based method, and the other is a likelihood based method. While a model is fitted by minimizing the sum of squared prediction errors of y`s and x`s in the classical and inverse methods, respectively. In the new distance based method, we simultaneously minimize the sum of both squared prediction errors. In the likelihood based method, we propose an inverse regression with Arnold-Beaver Skew Normal(ABSN) error distribution. Using the cross validation method with an asymmetric real data set, two new and two existing methods are studied based on the relative prediction bias(RBP) criteria.
 Keywords
Arnold-Beaver skew normal distribution;asymmetric data;inverse regression;calibration;relative prediction bias;
 Language
English
 Cited by
1.
Skew Normal Boxplot and Outliers,;;

Communications for Statistical Applications and Methods, 2012. vol.19. 4, pp.591-595 crossref(new window)
1.
Inverse circular–circular regression, Journal of Multivariate Analysis, 2013, 119, 200  crossref(new windwow)
2.
Skew Normal Boxplot and Outliers, Communications for Statistical Applications and Methods, 2012, 19, 4, 591  crossref(new windwow)
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