Recursive M-Estimators of Location and Scale for Dependent Sequences,

Abstract

Recursive M-estimators of location and scale may be obtained via stochastic approximation algorithms. We consider the case when the observations can be described by a strictly stationary process satisfying certain strong mixing conditions and results on strong convergence are given. The asymptotic distributions of the estimators for sequences of independent observations are also discussed.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1986
Accession Number
ADA186292

Entities

People

  • David Ruppert
  • Jan-eric Englund
  • Ulla Holst

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Convergence
  • Data Science
  • Estimators
  • Information Science
  • Normal Distribution
  • North Carolina
  • Notation
  • Observation
  • Probability
  • Random Variables
  • Sequences
  • Stationary
  • Stationary Processes
  • Statistical Algorithms
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Statistical inference.