On Time Series Analysis of Public Health and Biomedical Data

Scott Zeger, Rafael Irizarry and Roger Peng
Additional contact information
Scott Zeger: The Johns Hopkins Bloomberg School of Public Health
Rafael Irizarry: Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Roger Peng: Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics

No 1054, Johns Hopkins University Dept. of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: 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 used to describe the dependence of the response at each time on predictor variables including covariates and possibly previous values in the series. Time series methods are necessary to account for the correlation among repeated responses over time. This paper gives an overview of time series ideas and methods used in public health research.

Keywords: Stochastic process; Smoothing; Autocorrelation; Periodogram; Spectrum; Regression; Autoregressive model; ARMA; Non-linear time series (search for similar items in EconPapers)
Date: 2004-09-03
Note: oai:bepress.com:jhubiostat-1054
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