Efficient estimation of variance components in nonparametric mixed-effects models with large samples

Helwig Nathaniel E.

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Efficient estimation of variance components in nonparametric mixed-effects models with large samples

机译：大样本非参数混合效应模型中方差分量的有效估计

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Linear mixed-effects (LME) regression models are a popular approach for analyzing correlated data. Nonparametric extensions of the LME regression model have been proposed, but the heavy computational cost makes these extensions impractical for analyzing large samples. In particular, simultaneous estimation of the variance components and smoothing parameters poses a computational challenge when working with large samples. To overcome this computational burden, we propose a two-stage estimation procedure for fitting nonparametric mixed-effects regression models. Our results reveal that, compared to currently popular approaches, our two-stage approach produces more accurate estimates that can be computed in a fraction of the time.

机译：线性混合效应（LME）回归模型是一种用于分析相关数据的流行方法。已经提出了LME回归模型的非参数扩展，但是沉重的计算成本使得这些扩展对于分析大型样本不切实际。特别是，在处理大样本时，同时估计方差分量和平滑参数会带来计算难题。为了克服这一计算负担，我们提出了一个两阶段的估计程序来拟合非参数混合效应回归模型。我们的结果表明，与当前流行的方法相比，我们的两阶段方法可以产生更准确的估计值，而这些估计值可以在很短的时间内计算出来。

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来源
《Statistics and computing》 |2016年第6期|1319-1336|共18页
作者
Helwig Nathaniel E.;
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作者单位

Univ Minnesota Twin Cities, Dept Psychol, Minneapolis, MN 55455 USA|Univ Minnesota Twin Cities, Sch Stat, Minneapolis, MN 55455 USA;

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原文格式 PDF
正文语种 eng
中图分类
关键词
Algorithms; Linear mixed models; Nonparametric regression; REML; Smoothing splines; Variance components;

机译：算法;线性混合模型;非参数回归;REML;平滑样条;方差分量;

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1. Stochastic Lanczos estimation of genomic variance components for linear mixed-effects models [J] . Richard Border, Stephen Becker BMC Bioinformatics . 2019,第1期

机译：线性混合效应模型的基因组方差成分的随机Lanczos估计
2. Stochastic Lanczos estimation of genomic variance components for linear mixed-effects models [J] . Richard Border, Stephen Becker BMC Bioinformatics . 2019,第1期

机译：线性混合效应模型的基因组方差成分的随机Lanczos估计
3. Efficient estimation for error component seemingly unrelated nonparametric regression models [J] . Bin Zhou, Qinfeng Xu, Jinhong You Metrika . 2011,第1期

机译：误差分量看似无关的非参数回归模型的有效估计
4. Spline estimation for nonparametric variance models with linear process innovations [C] . Xinqian Wu, Wancai Yang, Shuangpeng Li 2011 International Conference on Consumer Electronics, Communications and Networks . 2011

机译：具有线性过程创新的非参数方差模型的样条估计
5. Efficient perfect and MCMC sampling methods for Bayesian spatial and components of variance models. [D] . Haran, Murali. 2003

机译：贝叶斯空间和方差模型分量的高效完美和MCMC采样方法。
6. Estimation of Variance Components in the Mixed-Effects Models: A Comparison Between Analysis of Variance and Spectral Decomposition [O] . Mi-Xia Wu, Kai-Fun Yu, Ai-Yi Liu -1

机译：混合效果模型中方差分量的估计：方差分析与光谱分解之间的比较
7. Stochastic Lanczos estimation of genomic variance components for linear mixed-effects models [O] . Richard Border, Stephen Becker 2019

机译：线性混合效应模型基因组方差组分的随机兰西

Efficient estimation of variance components in nonparametric mixed-effects models with large samples

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