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Estimation for Stochastic Models Driven by Laplace Motion

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Author

Summary, in English

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.

Publishing year

2011

Language

English

Pages

3281-3302

Publication/Series

Communications in Statistics: Theory and Methods

Volume

40

Issue

18

Document type

Journal article

Publisher

Marcel Dekker

Topic

  • Probability Theory and Statistics

Keywords

  • Kurtosis
  • Laplace distribution
  • Method of moment estimation
  • Moving
  • averages
  • Skewness
  • Stochastic fields

Status

Published

ISBN/ISSN/Other

  • ISSN: 0361-0926