Helfenstein U
Department of Biostatistics, University of Zurich, Switzerland.
Stat Methods Med Res. 1996 Mar;5(1):3-22. doi: 10.1177/096228029600500102.
Notifications of diseases, entries in a hospital, injuries due to accidents, etc., are frequently collected in fixed equally spaced intervals. Such observations are likely to be dependent. In environmental medicine, where series such as daily concentrations of pollutants are collected and analysed, it is evident that dependence of consecutive measurements may be important. A high concentration of a pollutant today has a certain 'inertia', i.e. a tendency to be high tomorrow as well. Dependence of consecutive observations may be equally important when data such as blood glucose are recorded within a single patient. ARIMA models (autoregressive integrated moving average models, Box-Jenkins models), which allow the stochastic dependence of consecutive data to be modelled, have become well established in such fields as economics. This article reviews basic concepts of Box-Jenkins modelling. The methods are illustrated by applications. In particular, the following topics are presented: the ARIMA model, transfer function models (assessment of relations between time series) and intervention analysis (assessment of changes of time series).
疾病通报、医院收治记录、事故所致伤害等，常常按固定的等间隔时间收集。此类观测值可能具有相关性。在环境医学中，会收集并分析诸如污染物日浓度之类的序列数据，显然连续测量值之间的相关性可能很重要。今日污染物浓度高具有一定的“惯性”，也就是说，明日浓度也往往较高。当在单个患者体内记录血糖等数据时，连续观测值之间的相关性可能同样重要。自回归积分滑动平均模型（ARIMA模型，也称博克斯 - 詹金斯模型）能够对连续数据的随机相关性进行建模，在经济学等领域已得到广泛应用。本文回顾了博克斯 - 詹金斯建模的基本概念，并通过应用实例对这些方法进行说明。特别介绍了以下主题：ARIMA模型、传递函数模型（时间序列之间关系的评估）以及干预分析（时间序列变化的评估）。
