Development of uncertainty-based work injury model using Bayesian structural equation modelling
Development of uncertainty-based work injury model using Bayesian structural equation modelling
复制标题
使用贝叶斯结构方程模型开发基于不确定性的工伤模型
DOI：
10.1080/17457300.2013.825629
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发表时间：
2014
影响因子：
2.3
通讯作者：
S. Chatterjee
中科院分区：
文献类型：
--
作者：
S. Chatterjee
本文针对印度地下煤矿提出了基于贝叶斯方法的矿工工伤结构方程模型（SEM）。确定了工伤的环境和行为变量并建立了因果关系。对于贝叶斯建模，SEM 参数的先验分布对于开发模型是必要的。本文采用两种方法获得SEM的因子载荷参数和结构参数的先验分布。在第一种方法中，先验分布被视为具有特定参数值的固定分布函数，而在第二种方法中，参数的先验分布是根据专家的意见生成的。这些参数的后验分布是通过应用贝叶斯规则获得的。采用吉布斯抽样形式的马尔可夫链蒙特卡罗抽样应用于后验分布的抽样。结果表明，结构和测量模型参数的所有系数在专家基于意见的先验中都具有统计显着性，而当应用固定的基于先验的分布时，两个系数不具有统计显着性。误差统计表明，与传统 SEM 相比，贝叶斯结构模型提供了相当好的工伤拟合，具有较高的确定系数 (0.91) 和较小的均方误差。
This paper proposed a Bayesian method-based structural equation model (SEM) of miners’ work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts’ opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts’ opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.
影响因子：
3.2
作者：
Carayon, Pascale
通讯作者：
Carayon, Pascale
DOI：
10.1111/j.2044-8317.1984.tb00789.x
发表时间：
1984-01-01
影响因子：
2.6
作者：
BROWNE, MW
通讯作者：
BROWNE, MW
影响因子：
22.4
作者：
HU, LT;BENTLER, PM;KANO, Y
通讯作者：
KANO, Y