A regression model for time series of counts

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)621-629
Number of pages9
JournalBiometrika
Volume75
Issue number4
DOIs
StatePublished - Dec 1988

Fingerprint

Log-linear Models
Estimating Equation
Least Square Algorithm
Poliomyelitis
Regression Coefficient
Least-Squares Analysis
Regression Analysis
Asymptotic Properties
Parameter Estimation
Time series
Predictors
time series analysis
Linear Models
Incidence
Regression Model
Count
linear models
Regression analysis
Parameter estimation
least squares

Keywords

  • Dependence
  • Estimating equation
  • Log linear
  • Parameter driven
  • Poisson
  • Quasilikelihood
  • Regression

ASJC Scopus subject areas

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

Cite this

A regression model for time series of counts. / Zeger, Scott.

In: Biometrika, Vol. 75, No. 4, 12.1988, p. 621-629.

Research output: Contribution to journalArticle

Zeger, Scott. / A regression model for time series of counts. In: Biometrika. 1988 ; Vol. 75, No. 4. pp. 621-629.
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