Modeling and smoothing unequally spaced sequence data

Piet De Jong, Sonia Mazzi

Research output: Contribution to journalArticlepeer-review


The application of the continuous state space model to unequally spaced sequence data is discussed and illustrated. The continuous model implies a discrete model for the observed data. Practical expressions for relevant discrete model quantities are given. These quantites are required for the digital processing of the data and in particular for the application of the Kalman and smoothing filter and related calculations. Applications illustrate the procedures.
Original languageEnglish
Pages (from-to)53-71
Number of pages19
JournalStatistical Inference for Stochastic Processes
Publication statusPublished - 2001


  • Kalman filter
  • splines
  • smoothing
  • state space model


Dive into the research topics of 'Modeling and smoothing unequally spaced sequence data'. Together they form a unique fingerprint.

Cite this