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非线性混合效应模型参数估计方法分析

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
非线性混合效应模型是针对回归函数依赖于固定效应和随机效应的非线性关系而建立的.一阶线性化算法(FO)和条件一阶线性化算法(FOCE)为2种计算非线性混合效应模型参数的常用线性化算法.本文基于FOCE算法,提出一种改进的随机效应参数计算方法,并利用树高生长数据和模拟数据对3种算法进行分析和比较.结果表明:改进的FOCE算法得到的随机效应参数更能反映个体间的随机差异,并且拟合效果更好.
Analysis of Nonlinear Mixed Effects Model Parameter Estimation Methods
Nonlinear mixed effects model ( NLMEM ) is the model in which both the fixed and random effects occur nonlinearly in the model function. First-order linearization algorithm ( FO ) and conditional first-order linearization algorithm (FOCE)are two commonly used linearization algorithms to calculate the parameters in NLMEM. We proposed an improved method for calculating random effects parameters based on FOCE algorithm in this study. We also analyzed and compared the three algorithms using height growth data set and simulation data sets. The results are; random effects parameters obtained from improved FOCE algorithm can more really reflected the individual random variations and also make a high efficient fit.

nonlinear mixed effects model (NLMEM)first-order linearization algorithm ( FO)conditional first-order linearization algorithm(FOCE)improved FOCE algorithm

符利勇、张会儒、李春明、唐守正

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中国林业科学研究院资源信息研究所 北京10091

非线性混合效应模型 一阶线性化算法(FO) 条件一阶线性化算法(FOCE) 改进的FOCE算法

林业公益性行业重点项目

201004002

2013

10.11707/j.1001-7488.20130117

林业科学
中国林学会

林业科学

CSTPCDCSCD北大核心
影响因子：1.272
ISSN：1001-7488
年,卷(期)：2013.49(1)
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