Heteroscedastic regression analysis method for mixed data
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Heteroscedastic regression analysis method for mixed data
Heteroscedastic regression analysis method for mixed data
航空动力学报2011年26卷第4期
作者机构:
Research Center of Small Sample Technology, Beijing University of Aeronautics and Astronautics,Beijing,China,100191
作者简介:
基金信息:
Supported by the National Natural Science Foundation of China (Grant No.10472006)Supported by the National Natural Science Foundation of China (Grant No.10472006)
DOI:
中图分类号:
纸质出版:2011
稿件说明:
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FU Hui-min, YUE Xiao-rui. Heteroscedastic regression analysis method for mixed data[J]. 航空动力学报, 2011,26(4).
FU Hui-min, YUE Xiao-rui. Heteroscedastic regression analysis method for mixed data[J]. 2011, 26(4).
FU Hui-min, YUE Xiao-rui. Heteroscedastic regression analysis method for mixed data[J]. 航空动力学报, 2011,26(4).DOI:
FU Hui-min, YUE Xiao-rui. Heteroscedastic regression analysis method for mixed data[J]. 2011, 26(4).DOI:
Heteroscedastic regression analysis method for mixed data
The heteroscedastic regression model was established and the heteroscedastic regression analysis method was presented for mixed data composed of complete data
type-Ⅰ censored data and type-Ⅱ censored data from the location-scale distribution. The best unbiased estimations of regression coefficients
as well as the confidence limits of the location parameter and scale parameter were given. Furthermore
the point estimations and confidence limits of percentiles were obtained. Thus
the traditional multiple regression analysis method which is only suitable to the complete data from normal distribution can be extended to the cases of heteroscedastic mixed data and the location-scale distribution. So the presented method has a broad range of promising applications.
Abstract
The heteroscedastic regression model was established and the heteroscedastic regression analysis method was presented for mixed data composed of complete data
type-Ⅰ censored data and type-Ⅱ censored data from the location-scale distribution. The best unbiased estimations of regression coefficients
as well as the confidence limits of the location parameter and scale parameter were given. Furthermore
the point estimations and confidence limits of percentiles were obtained. Thus
the traditional multiple regression analysis method which is only suitable to the complete data from normal distribution can be extended to the cases of heteroscedastic mixed data and the location-scale distribution. So the presented method has a broad range of promising applications.