Predicting Transforms of Stable Noise and other Gaussian Mixtures.

Abstract

Stationary stable processes that are Fourier transforms of symmetric stable independent increments processes are shown to have a.s. finite conditional expectation of X sub t given X sub s and conditional variance of X sub t given X sub(t - delta), X sub(t -2 delta). The associated conditional expectation predictors are nonlinear in (X sub s, s<t) but are mixtures of predictors of the usual type based on the Gaussian model.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1987
Accession Number
ADA189280

Entities

People

  • Raoul Lepage

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Data Science
  • Equations
  • Gaussian Processes
  • Hilbert Space
  • Information Science
  • Integrals
  • Noise
  • Order Statistics
  • Probability
  • Probability Distributions
  • Random Variables
  • Real Numbers
  • Sequences
  • Stationary Processes
  • Statistics
  • Stochastic Processes
  • Theorems

Readers

  • Statistical inference.