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摘要翻译：
研究了数量性状遗传作图中基于限制极大似然(REML)模型的方差分量估计的稳健有效的优化方法。我们证明了当最优解位于其中一个约束边界时，标准Newton-AI格式可能会失败，并通过考虑约束条件引入不同的方法来弥补这一缺陷。我们用平均信息矩阵和BFGS逆公式来逼近目标函数的Hessian。对来自同一动物种群的两个实验数据所产生的问题进行了鲁棒性和效率评估。
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英文标题：
《Newton-type Methods for REML Estimation in Genetic Analysis of
Quantitative Traits》
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作者：
Kateryna Mishchenko, Sverker Holmgren, Lars Ronnegard
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最新提交年份：
2007
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Quantitative Biology 数量生物学
二级分类：Quantitative Methods 定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
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英文摘要：
Robust and efficient optimization methods for variance component estimation using Restricted Maximum Likelihood (REML) models for genetic mapping of quantitative traits are considered. We show that the standard Newton-AI scheme may fail when the optimum is located at one of the constraint boundaries, and we introduce different approaches to remedy this by taking the constraints into account. We approximate the Hessian of the objective function using the average information matrix and also by using an inverse BFGS formula. The robustness and efficiency is evaluated for problems derived from two experimental data from the same animal populations.
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PDF链接：
https://arxiv.org/pdf/0711.2619
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