Threshold selection in jump-discriminant filter for discretely observed jump processes

Yasutaka Shimizu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this method, a jump-discriminant filter is used to infer the continuous part and the jump part separately. Although there are several choices for the filter, statistics constructed via filters are often sensitive to the choice. This paper presents some numerical procedures for selecting a suitable filter based on observations.

Original languageEnglish
Pages (from-to)355-378
Number of pages24
JournalStatistical Methods and Applications
Issue number3
Publication statusPublished - 2010
Externally publishedYes


  • Asymptotic unbiasedness
  • Integrated-volatility
  • Jump-discriminant filter
  • Plug-in method
  • Threshold estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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