Sieves for Gaussian Processes.

Abstract

The PI has established several important properties of a proposed sieve estimator for the mean of a Gaussian process of known covariance: The estimator is itself a Gaussian process; under a separability assumption, it is asymptotically unbiased and weakly consistent at each time t, and is strongly consistent (globally) in an appropriate norm. For a Gaussian process with zero mean and unknown covariance, the PI has shown that the likelihood for the covariance is in general unbounded almost surely. Moreover, he has developed properties of a proposed sieve estimator for the covariance analogous to those for the mean. No assumption is made about the nature of the time parameter t, either for the mean estimator or for the covariance estimator. Keywords: Hilbert space; Maximum likelihood estimation. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Nov 27, 1985
Accession Number
ADA166055

Entities

People

  • Jay H. Beder

Organizations

  • University of Wisconsin–Milwaukee

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Classification
  • Covariance
  • Data Science
  • Estimators
  • Gaussian Processes
  • Hilbert Space
  • Information Science
  • Maximum Likelihood Estimation
  • Probability
  • Random Variables
  • Security
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Linear Algebra
  • Mathematical Modeling and Probability Theory.
  • Regression Analysis.

Technology Areas

  • Space