• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Biometrics: Journal of the Biometric Society : An International Society Devoted to the Mathematical and Statistical Aspects of Biology >A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error
【24h】

A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error

机译：具有协变量误差的非线性回归模型的正则化校正得分方法

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

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer.
机译：许多回归分析都包含解释性变量，这些变量具有误差，而众所周知，未能解决该误差会导致回归系数的偏差点和区间估计。我们在这里提出了一种用于调整协变量误差的新通用方法。我们的方法由Stefanski-Nakamura校正分数方法的近似版本组成，使用正则化方法来获得相关积分方程的近似解。我们在经典似然模型的设置中发展了该理论;此设置包括例如线性回归，非线性回归，逻辑回归和泊松回归。就所涵盖的测量误差模型的类型而言，该方法非常通用，并且在不涉及对真实协变量分布的假设的意义上，它是一种功能方法。我们讨论了该方法的理论特性，并在逻辑回归设置（单变量和多变量）中给出了模拟结果。为说明起见，我们将该方法应用于哈佛护士健康研究的数据，该数据涉及乳腺癌诊断后身体活动与乳腺癌死亡率之间的关系。

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