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On Time Series Analysis of Public Health and Biomedical Data

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Author Info
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)
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.

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File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1054&context=jhubiostat
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Publisher Info
Paper provided by Berkeley Electronic Press in its series Johns Hopkins University Dept. of Biostatistics Working Paper Series with number 1054.

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Date of creation: 03 Sep 2004
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Handle: RePEc:bep:jhubio:1054

Note: oai:bepress.com:jhubiostat-1054
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Web page: http://www.bepress.com

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Related research
Keywords: Stochastic process; Smoothing; Autocorrelation; Periodogram; Spectrum; Regression; Autoregressive model; ARMA; Non-linear time series;

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This page was last updated on 2009-12-15.


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