Least Squares Estimators in a Stationary Random Field

Abstract

A particular two dimensional model in a stationary random field, which has 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
Nov 11, 1999
Accession Number
ADA379348

Entities

People

  • D. Kundu
  • S. Nandi

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Classification
  • Consistency
  • Data Science
  • Estimators
  • Information Science
  • Mathematics
  • Maximum Likelihood Estimation
  • New York
  • Probability
  • Random Variables
  • Real Numbers
  • Signal Processing
  • Stationary
  • Statistical Algorithms
  • Statistics
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Calculus or Mathematical Analysis
  • Computational Modeling and Simulation
  • Computer Vision.