ISMDA 2001: Medical Data Analysis pp 81-87 | Cite as

Special Time Series Models for Analysis of Mortality Data

  • Maria Fazekas
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2199)

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.

Keywords

Autocorrelation Function Time Series Analysis Mortality Data ARIMA Model Stationary Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Maria Fazekas
    • 1
  1. 1.Department of Agroinformatics and Applied MathematicsDebrecen UniversityDebrecenHungary

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