Regression analysis with censored autocorrelated data

Scott Zeger, Ron Brookmeyer

Research output: Contribution to journalArticle

Abstract

For many studies in which data are collected sequentially in time, the sensitivity of the measurement is limited and an exact value can be recorded only if it falls within a specified range. This gives rise to a censored time series. In this article, we present a methodology for regression analysis of censored time series data. We fit autoregressive models to account for the time dependence. Two numerical methods for full likelihood estimation and an approximate method are discussed. The methods are illustrated with air pollution data subject to lower limits of detection.

Original languageEnglish (US)
Pages (from-to)722-729
Number of pages8
JournalJournal of the American Statistical Association
Volume81
Issue number395
DOIs
StatePublished - 1986

Fingerprint

Censored Data
Regression Analysis
Air Pollution
Time Dependence
Autoregressive Model
Time Series Data
Likelihood
Time series
Numerical Methods
Methodology
Range of data
Regression analysis
Censored data
Time series data
Air pollution
Time dependence
Likelihood estimation
Autoregressive model
Numerical methods

Keywords

  • Autoregressive
  • EM algorithm
  • Gaussian
  • Pseudolikelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Regression analysis with censored autocorrelated data. / Zeger, Scott; Brookmeyer, Ron.

In: Journal of the American Statistical Association, Vol. 81, No. 395, 1986, p. 722-729.

Research output: Contribution to journalArticle

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