Kis Maria
Debrecen University, Boszormenyi ut 138, H-4032 Debrecen, Hungary.
Stud Health Technol Inform. 2002;90:439-43.
The mortality data may be analysed by time series methods such as autoregressive integrated moving average (ARIMA) modelling. This method is demonstrated by two examples: analysis of the mortality data of cerebrovascular diseases and analysis of the mortality data of cancer of cervix. Mathematical expressions are given for the results of analysis. The relationships between time series of mortality rates were studied with ARIMA models. Calculations of confidence intervals for autoregressive parameters by tree methods: standard normal distribution as estimation and estimation of the White's theory and the continuous time case estimation. Analysing the confidence intervals of the first order autoregressive parameters we may conclude that the confidence intervals were much smaller than other estimations by applying the continuous time estimation model.
死亡率数据可以通过时间序列方法进行分析，如自回归积分滑动平均（ARIMA）建模。通过两个例子展示了这种方法：脑血管疾病死亡率数据的分析和宫颈癌死亡率数据的分析。给出了分析结果的数学表达式。使用ARIMA模型研究了死亡率时间序列之间的关系。通过三种方法计算自回归参数的置信区间：以标准正态分布作为估计、怀特理论的估计以及连续时间情况的估计。分析一阶自回归参数的置信区间，我们可以得出结论，通过应用连续时间估计模型，置信区间比其他估计要小得多。
