• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Computational and Mathematical Methods in Medicine >Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error
【2h】

Multiple Linear Regressions by Maximizing the Likelihood under Assumption of Generalized Gauss-Laplace Distribution of the Error

机译：在误差的广义高斯-拉普拉斯分布假设下最大化似然性的多元线性回归

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Multiple linear regression analysis is widely used to link an outcome with predictors for better understanding of the behaviour of the outcome of interest. Usually, under the assumption that the errors follow a normal distribution, the coefficients of the model are estimated by minimizing the sum of squared deviations. A new approach based on maximum likelihood estimation is proposed for finding the coefficients on linear models with two predictors without any constrictive assumptions on the distribution of the errors. The algorithm was developed, implemented, and tested as proof-of-concept using fourteen sets of compounds by investigating the link between activity/property (as outcome) and structural feature information incorporated by molecular descriptors (as predictors). The results on real data demonstrated that in all investigated cases the power of the error is significantly different by the convenient value of two when the Gauss-Laplace distribution was used to relax the constrictive assumption of the normal distribution of the error. Therefore, the Gauss-Laplace distribution of the error could not be rejected while the hypothesis that the power of the error from Gauss-Laplace distribution is normal distributed also failed to be rejected.
机译：多元线性回归分析被广泛用于将结果与预测变量联系起来，以更好地了解目标结果的行为。通常，在假设误差服从正态分布的前提下，通过最小化平方差之和来估算模型的系数。提出了一种基于最大似然估计的新方法，该方法用于在具有两个预测变量的线性模型上找到系数，而无需对误差分布进行任何限制性假设。通过研究活性/性质（作为结果）与分子描述符所包含的结构特征信息（作为预测因子）之间的联系，使用十四组化合物开发，实施并测试了该算法，作为概念验证。实际数据的结果表明，在所有调查的情况下，当使用高斯-拉普拉斯分布来放松误差的正态分布的狭义假设时，误差的乘方因两个便利值而显着不同。因此，误差的高斯-拉普拉斯分布不能被拒绝，而高斯-拉普拉斯分布的误差的幂是正态分布的假设也不能被拒绝。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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