• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>The American statistician >Compound Regression and Constrained Regression: Nonparametric Regression Frameworks for EIV Models
【24h】

Compound Regression and Constrained Regression: Nonparametric Regression Frameworks for EIV Models

机译：复合回归和约束回归：EIV模型的非参数回归框架

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

Errors-in-variable (EIV) regression is often used to gauge linear relationship between two variables both suffering from measurement and other errors, such as, the comparison of two measurement platforms (e.g., RNA sequencing vs. microarray). Scientists are often at a loss as to which EIV regression model to use for there are infinite many choices. We provide sound guidelines toward viable solutions to this dilemma by introducing two general nonparametric EIV regression frameworks: the compound regression and the constrained regression. It is shown that these approaches are equivalent to each other and, to the general parametric structural modeling approach. The advantages of these methods lie in their intuitive geometric representations, their distribution free nature, and their ability to offer candidate solutions with various optimal properties when the ratio of the error variances is unknown. Each includes the classic nonparametric regression methods of ordinary least squares, geometric mean regression (GMR), and orthogonal regression as special cases. Under these general frameworks, one can readily uncover some surprising optimal properties of the GMR, and truly comprehend the benefit of data normalization.for this article are available online.
机译：变量错误（EIV）回归通常用于衡量患有测量和其他误差的两个变量之间的线性关系，例如，两个测量平台的比较（例如，RNA测序与微阵列）。科学家们往往是损失，因为它对于哪种EIV回归模型用于有无限的选择。通过引入两个普通非参数EIV回归框架，我们为这种困境提供合理指南，通过引入两种普通的非参数回归框架：复合回归和受约束的回归。结果表明，这些方法相当于彼此，并且到一般参数结构建模方法。这些方法的优点在于它们直观的几何表示，它们的分配自然，以及当误差方差的比率未知时提供具有各种最佳特性的候选解决方案的能力。每个包括普通最小二乘的经典非参数回归方法，几何平均回归（GMR）和正交回归作为特殊情况。在这些一般框架下，人们可以易于揭示GMR的一些令人惊讶的最佳属性，并真正理解数据归一化的好处。对于本文可在线获得。

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