Rina Wijayanti
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引用次数: 0

摘要

在估计理论中,有两种方法,即经典统计方法和全局统计方法(贝叶斯)。经典统计是一种统计方法,其中的决策过程仅基于从总体中获取的数据样本。而贝叶斯统计则是根据观察到的数据(样本)和先验知识中的新信息做出决策。在撰写本文时,将以Cox回归分析为例,通过经典统计方法进行参数估计,生存分析和贝叶斯统计方法作为全局(贝叶斯)的例子。生存贝叶斯参数估计采用MCMC算法求解模型复杂/复杂,而Cox回归模型采用部分似然方法求解。参数估计的结果与牛顿-拉夫逊迭代法的估计结果并不接近。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PENAKSIRAN PARAMETER ANALISIS REGRESI COX DAN ANALISIS SURVIVAL BAYESIAN
In the theory of estimation, there are two approaches, namely the classical statistical approach and global statistical approach (Bayesian). Classical statistics are statistics in which the procedure is the decision based only on the data samples taken from the population. While Bayesian statistics in making decisions based on new information from the observed data (sample) and prior knowledge. At this writing Cox Regression Analysis will be taken as an example of parameter estimation by the classical statistical approach Survival Analysis and Bayesian statistical approach as an example of global (Bayesian). Survival Bayesial parameter estimation using MCMC algorithms for model complex / complicated and difficult to resolve while the Cox regression models using the method of partial likelihood. Results of the parameter estimates do not close form that needs to be done by the method of Newton-Raphson iteration.
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