Skip to main content

Estimating Trends in Tree-Ring Data

The full text article is temporarily unavailable.

We apologise for the inconvenience. Please try again later.

Two methods from econometrics are introduced to estimate growth trends in time series of ring widths or basal-area increments. First, a trend model is described with a stochastic level and slope. The second model combines a doubly differenced trend and an ARMA model additively. Both models are put into a state-space form and are estimated using the discrete Kalman filter. Unknown noise variances, which control the flexibility of the trends, can be estimated by maximum-likelihood optimization or chosen by hand. It is concluded that the trend plus AR (1) model in combination with ML estimation performs very well. This model is attractive, because the ML-estimation procedure enables an objective choice for unknown parameters. Examples are given of two special features: the prediction of future growth, and the weighing of missing or unreliable data. Finally, both models are compared with spline interpolation and are validated by means of simulated time series. For. Sci. 36(1):87-100.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: ARIMA models; Kalman filter; air pollution; dendroclimatology; growth curves; maximum likelihood

Document Type: Journal Article

Affiliations: Applied Mathematician, Mathematics Consulting Department, Eindhoven University, P.O. Box 513, 5600 MB Eindhoven, The Netherlands

Publication date: 01 March 1990

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more