On time series analysis of public health and biomedical data

Scott L. Zeger, Rafael Irizarry, Roger D. Peng

Research output: Contribution to journalReview article

Abstract

This paper gives an overview of time series ideas and methods used in public health and biomedical research. A time series is a sequence of observations made over time. Examples in public health include daily ozone concentrations, weekly admissions to an emergency department, or annual expenditures on health care in the United States. Time series models are most commonly used in regression analysis to describe the dependence of the response at each time on predictor variables including covariates and possibly previous values in the series. For example, Bell et al. (2) use time series methods to regress daily mortality in U.S. cities on concentrations of particulate air pollution. Time series methods are necessary to make valid inferences from data by accounting for the correlation among repeated responses over time.

Original languageEnglish (US)
Pages (from-to)57-79
Number of pages23
JournalAnnual Review of Public Health
Volume27
DOIs
StatePublished - Apr 24 2006

Keywords

  • ARMA
  • Autocorrelation
  • Autoregressive model
  • Nonlinear time series
  • Periodogram
  • Regression
  • Smoothing
  • Spectrum
  • Stochastic process

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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