The analysis of nonlinear function-valued characters is very important in genetic studies, especially for growth traits of agricultural and laboratory species. Inference in nonlinear mixed effects models is, however, quite complex and is usually based on likelihood approximations or Bayesian methods. The aim of this paper was to present an efficient stochastic EM procedure, namely the SAEM algorithm, which is much faster to converge than the classical Monte Carlo EM algorithm and Bayesian estimation procedures, does not require specification of prior distributions and is quite robust to the choice of starting values. The key idea is to recycle the simulated values from one iteration to the next in the EM algorithm, which considerably accelerates the convergence. A simulation study is presented which confirms the advantages of this estimation procedure in the case of a genetic analysis. The SAEM algorithm was applied to real data sets on growth measurements in beef cattle and in chickens. The proposed estimation procedure, as the classical Monte Carlo EM algorithm, provides significance tests on the parameters and likelihood based model comparison criteria to compare the nonlinear models with other longitudinal methods.
展开▼
机译：非线性函数值特征的分析在遗传研究中非常重要，特别是对于农业和实验室物种的生长性状。但是，非线性混合效应模型的推论非常复杂，通常基于似然近似或贝叶斯方法。本文的目的是提出一种有效的随机EM程序，即SAEM算法，其收敛速度比经典的Monte Carlo EM算法和贝叶斯估计程序快得多，不需要先验分布的规范，并且对起始值的选择。关键思想是在EM算法中将仿真值从一次迭代循环到下一次迭代，从而大大加快了收敛速度。进行了仿真研究，证实了在遗传分析的情况下该估算程序的优势。 SAEM算法已应用于肉牛和鸡生长测量的真实数据集。作为经典的蒙特卡洛EM算法，所提出的估计程序对参数和基于似然性的模型比较标准进行了显着性检验，以将非线性模型与其他纵向方法进行比较。
展开▼
