Abstract
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 diseases of digestive system and analysis of the mortality data of bronchitis, emphysema and asthma. 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 three methods: standard normal distribution as estimation, the estimation of the White’s theory and the continuous time 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.
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Fazekas, M. (2001). Special Time Series Models for Analysis of Mortality Data. In: Crespo, J., Maojo, V., Martin, F. (eds) Medical Data Analysis. ISMDA 2001. Lecture Notes in Computer Science, vol 2199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45497-7_12
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DOI: https://doi.org/10.1007/3-540-45497-7_12
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