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http://dx.doi.org/10.5351/KJAS.2010.23.4.759

New Calibration Methods with Asymmetric Data  

Kim, Sung-Su (Department of Statistics, Kyungpook National University)
Publication Information
The Korean Journal of Applied Statistics / v.23, no.4, 2010 , pp. 759-765 More about this Journal
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;
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