Fixed Accuracy Estimation of an Autoregressive Parameter.

Abstract

For a first order non-explosive autoregressive process with unknown parameter beta epsilon (1,1), it is shown that if data are collected according to a particular stopping rule, the least squares estimator of beta is asymptotically normally distributed uniformly in beta. In the case of normal residuals, the stopping rule may be interpreted as sampling until the observed Fisher information reaches a preassigned level. The situation is contrasted with the fixed sample size case, where the estimator has a non-normal limiting distribution when (beta) = 1. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
May 01, 1982
Accession Number
ADA119373

Entities

People

  • David Siegmund
  • T. L. Lai

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Asymptotic Normality
  • Brownian Motion
  • Convergence
  • Data Science
  • Estimators
  • Explosives
  • Information Science
  • Intervals
  • Normality
  • Probability
  • Random Variables
  • Sampling
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.