Estimation for Stochastic Models Driven by Laplace Motion

Research output: Contribution to journalArticle

Abstract

Laplace motion is a Levy process built upon Laplace distributions. Non Gaussian stochastic fields that are integrals with respect to this process are considered and methods for their model fitting are discussed. The proposed procedures allow for inference about the parameters of the underlying Laplace distributions. A fit of dependence structure is also addressed. The importance of a convenient parameterization that admits natural and consistent estimation for this class of models is emphasized. Several parameterizations are introduced and their advantages over one another discussed. The proposed estimation method targets the standard characteristics: mean, variance, skewness and kurtosis. Their sample equivalents are matched in the closest possible way as allowed by natural constraints within this class. A simulation study and an example of potential applications conclude the article.

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Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Probability Theory and Statistics

Keywords

  • Kurtosis, Laplace distribution, Method of moment estimation, Moving, averages, Skewness, Stochastic fields
Original languageEnglish
Pages (from-to)3281-3302
JournalCommunications in Statistics: Theory and Methods
Volume40
Issue number18
StatePublished - 2011
Peer-reviewedYes