• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Scientia Africana: An International Journal of Pure & Applied Sciences >MANLY TRANSFORMATION IN QUANTILE REGRESSION: A COMPARISON OF TWO TRANSFORMATION PARAMETER ESTIMATORS
【24h】

MANLY TRANSFORMATION IN QUANTILE REGRESSION: A COMPARISON OF TWO TRANSFORMATION PARAMETER ESTIMATORS

机译：分位数回归中的男性转换：两个转换参数估计器的比较

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This study implements the Manly transformations for normalization of variables in quantile regression analysis. The transformation parameter was estimated using two different methods namely; the maximum likelihood estimation (MLE) method and the two-step estimation method by Chamberlain and Buchinsky(CBTS). The transformation parameters obtained using the two different methods were used for the Manly transformation of data with outliers and data without outliers. The methods were applied to a quantile regression analysis at different quantiles (0.25, 0.50, 0.75, 0.95). Based on our findings, for data without outliers, the 25th quantile model was seen to be the best fit model compared to the other quantiles for the CBTS method with AIC=-43.46279, BIC=20.75212 and MSE=0.70956, while for the MLE the 50th quantile model was seen to be the best fit model with AIC=348.3657, BIC-20.13548, and MSE=O.00864. Considering data with outliers the 25th quantile model was still seen to be the best fit model compared to the other quantiles for the CBTS method with AIC=-48.5671, BIC=21.8321 and MSE=0.92341, while for the MLE the 50th quantile model was still seen to be the best fit model with A1C=988.6763, BIC=710.09, and MSE=690.7965. Comparison of both methods for data without outliers the study concludes that the estimation of the transformation parameter using the MLE produced better results with lower A1C, BIC and MSE at all quantiles and for data with outliers the study concludes that the estimation of the transformation parameter using CBTS produced better results with lower A1C, BIC and MSE results as is shown in table (3.5) and table (3.6) respectively.
机译：这项研究实现了在分位数回归分析中变量归一化的男子化转化。使用两种不同的方法估算转换参数。 Chamberlain和Buchinsky（CBTS）的最大似然估计方法（MLE）方法和两步估计方法。使用两种不同方法获得的转换参数用于与没有异常值的异常值和数据的男子气概转换。将这些方法应用于不同分位数下的分位数回归分析（0.25、0.50、0.75、0.95）。根据我们的发现，对于没有离群值的数据，与其他aic = -43.46279，BIC = 20.75212和MSE = 0.70956的CBTS方法相比，第25个分位数模型被认为是最佳拟合模型，而对于MLE第50分位数模型被认为是AIC = 348.3657，BIC-20.13548和MSE = O.00864的最佳拟合模型。考虑到与异常值的数据，与其他分位数相比，与其他分位数相比，第25个位点模型仍然是最佳拟合模型，而CBTS方法具有AIC = -48.5671，BIC = 21.8321和MSE = 0.92341，而对于MLE来说，50个位点模型仍然是认为是A1C = 988.6763，BIC = 710.09和MSE = 690.7965的最佳拟合模型。研究两种无异常值的数据方法的比较，研究得出的结论是，使用MLE对转换参数的估计在所有分位数中都产生了更好的结果，并且在所有分位数中都有较低的a1c，biC和MSE的估计，以及带有异常值的数据，研究得出的结论是，研究得出的结论是，使用转换参数估算了转换参数如表（3.5）和表（3.6）所示，CBT在较低的A1C，BIC和MSE结果下产生了更好的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号