Censored time series analysis with autoregressive moving average models

Jung Wook Park*, Marc Genton, Sujit K. Ghosh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

The authors consider time series observations with data irregularities such as censoring due to a detection limit. Practitioners commonly disregard censored data cases which often result in biased estimates. The authors present an attractive remedy for handling autocorrelated censored data based on a class of autoregressive and moving average (ARMA) models. In particular, they introduce an imputation method well suited for fitting ARMA models in the presence of censored data. They demonstrate the effectiveness of their technique in terms of bias, efficiency, and information loss. They also describe its adaptation to a specific context of meteorological time series data on cloud ceiling height, which are measured subject to the detection limit of the recording device.

Original languageEnglish (US)
Pages (from-to)151-168
Number of pages18
JournalCanadian Journal of Statistics
Volume35
Issue number1
DOIs
StatePublished - Jan 1 2007

Keywords

  • Censored time series
  • Fisher information
  • Gibbs sampler
  • Imputation
  • Truncated multivariate normal distribution

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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