A regression model for time series of counts

Scott L. Zeger

doi:10.1093/biomet/75.4.621

A regression model for time series of counts

Scott L. Zeger

Bloomberg School of Public Health

Research output: Contribution to journal › Article › peer-review

344 Scopus citations

Abstract

This paper discusses a model for regression analysis with a time series of counts. Correlation is assumed to arise from an unobservable process added to the linear predictor in a log linear model. An estimating equation approach used for parameter estimation leads to an iterative weighted and filtered least-squares algorithm. Asymptotic properties for the regression coefficients are presented. We illustrate the technique with an analysis of trends in U.S. polio incidence since 1970.

Original language	English (US)
Pages (from-to)	621-629
Number of pages	9
Journal	Biometrika
Volume	75
Issue number	4
DOIs	https://doi.org/10.1093/biomet/75.4.621
State	Published - Dec 1988

Keywords

Dependence
Estimating equation
Log linear
Parameter driven
Poisson
Quasilikelihood
Regression

ASJC Scopus subject areas

Statistics and Probability
General Mathematics
Agricultural and Biological Sciences (miscellaneous)
General Agricultural and Biological Sciences
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1093/biomet/75.4.621

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title = "A regression model for time series of counts",

abstract = "This paper discusses a model for regression analysis with a time series of counts. Correlation is assumed to arise from an unobservable process added to the linear predictor in a log linear model. An estimating equation approach used for parameter estimation leads to an iterative weighted and filtered least-squares algorithm. Asymptotic properties for the regression coefficients are presented. We illustrate the technique with an analysis of trends in U.S. polio incidence since 1970.",

keywords = "Dependence, Estimating equation, Log linear, Parameter driven, Poisson, Quasilikelihood, Regression",

author = "Zeger, {Scott L.}",

note = "Funding Information: The author acknowledges support from the Australian Commonwealth Scientific and Industrial Research Organization, Division of Mathematics and Statistics, the U.S. National Institute of Mental Health, and the U.S. National Institute of Health. Thanks are due to a referee whose comments substantially improved an earlier draft.",

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Y1 - 1988/12

N2 - This paper discusses a model for regression analysis with a time series of counts. Correlation is assumed to arise from an unobservable process added to the linear predictor in a log linear model. An estimating equation approach used for parameter estimation leads to an iterative weighted and filtered least-squares algorithm. Asymptotic properties for the regression coefficients are presented. We illustrate the technique with an analysis of trends in U.S. polio incidence since 1970.

AB - This paper discusses a model for regression analysis with a time series of counts. Correlation is assumed to arise from an unobservable process added to the linear predictor in a log linear model. An estimating equation approach used for parameter estimation leads to an iterative weighted and filtered least-squares algorithm. Asymptotic properties for the regression coefficients are presented. We illustrate the technique with an analysis of trends in U.S. polio incidence since 1970.

KW - Dependence

KW - Estimating equation

KW - Log linear

KW - Parameter driven

KW - Poisson

KW - Quasilikelihood

KW - Regression

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SP - 621

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JO - Biometrika

JF - Biometrika

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ER -

A regression model for time series of counts

Abstract

Keywords

ASJC Scopus subject areas

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