On Asymptotic Properties of the Least Squares Estimates in a Stationary Random Field

Abstract

A particular two dimensional model in a stationary random field, which has a wide applications in statistical signal processing and in texture classifications, is considered. We prove the consistency and also obtain the asymptotic distributions of the least squares estimators of the different model parameters. It is observed that the asymptotic distribution of the least squares estimators are multivariate normal. Some numerical experiments are performed to see how the asymptotic results work for finite samples. We propose some open problems at the end.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1999
Accession Number
ADA369822

Entities

People

  • Debasis Kundu
  • Swata Nandi

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Classification
  • Consistency
  • Data Science
  • Estimators
  • Information Science
  • Mathematics
  • Probability
  • Random Variables
  • Sequences
  • Signal Processing
  • Stationary
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Theorems
  • Two Dimensional
  • United States

Fields of Study

  • Mathematics

Readers

  • Computer Vision.
  • Statistical inference.