摘要
文章研究了普通变量正态云系数线性回归模型的问题。根据正态云的有关理论,并结合模糊随机变量的相关理论,推证了正态云的线性运算性质,在机会测度切贝雪夫不等式的基础上,建立了普通变量正态云系数线性回归优化模型,对因变量的可能取值,由定性系数所决定的定性概念,做出不确定性统计推断。对工作绩效数据的实证分析表明,模型预测实用有效。完善了定性概念线性回归预测的理论,进一步拓展了线性回归模型的研究内容。
This paper studies the normal cloud coefficient linear regression model of ordinary variables. This paper accords to the theory of normal cloud and combines the theory of fuzzy random variables to propose the linear operation property of normal cloud. Based on the Chebyshev inequality of chance measure, a normal cloud coefficient linear regression optimization model for ordinary variables is established. For the possible value of dependent variable, the uncertainty statistical inference is made by the qualitative concept determined by qualitative coefficient. The empirical analysis on job performance data shows that the model prediction is practical and effective. The theory of qualitative concept linear regression prediction is perfected, and the research content of linear regression model is further expanded.
作者
刘兆君
Liu Zhaojun(School of Mathematics and Information Science,Shandong Technology and Business University,Yantai Shandong 264005,China)
出处
《统计与决策》
CSSCI
北大核心
2022年第8期43-46,共4页
Statistics & Decision
关键词
正态云
双重随机变量
模糊随机变量
机会测度
线性回归模型
normal cloud
birandom variable
fuzzy random variable
chance measure
linear regression model
