文章基本信息

标题：Analysis of quantile regression as alternative to ordinary least squares
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作者：Ibrahim Abdullahi ; Abubakar Yahaya
期刊名称：International Journal of Advanced Statistics and Probability
电子版ISSN：2307-9045
出版年度：2015
卷号：3
期号：2
页码：138-145
DOI：10.14419/ijasp.v3i2.4686
出版社：Journal of Advanced Computer Science & Technology
摘要：In this article, an alternative to ordinary least squares (OLS) regression based on analytical solution in the Statgraphics software is considered, and this alternative is no other than quantile regression (QR) model. We also present goodness of fit statistic as well as approximate distributions of the associated test statistics for the parameters. Furthermore, we suggest a goodness of fit statistic called the least absolute deviation (LAD) coefficient of determination. The procedure is well presented, illustrated and validated by a numerical example based on publicly available dataset on fuel consumption in miles per gallon in highway driving.
关键词：Quantile Regression;Model Validation;Stepwise Regression;Linear Programming.