Minimum distance estimation and goodness-of-fit tests in first-order autoregression
type: Item
instance of: scholarly article
title: Minimum distance estimation and goodness-of-fit tests in first-order autoregression
zbMATH Open document ID: 0607.62101
author: Hira L. Koul
published in: The Annals of Statistics
publication date: 1986-01-01T00:00:00Z
review text: Consider the first-order autoregressive model \(X_ i=\rho X_{i- 1}+\epsilon_ i\), where the \(\{\epsilon_ i\}\) are i.i.d. according to a df F, symmetric about zero. The paper considers a minimum distance estimator of \(\rho\), within a certain class determined by a finite measure on the Borel line. In this class an asymptotically efficient estimator is exhibited. The paper also discusses goodness-of-fit tests of symmetry and for a specified error distribution.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62G05
Mathematics Subject Classification ID: 62G10
Mathematics Subject Classification ID: 62G20
zbMATH DE Number: 3982343
zbMATH Keywords: least-squares estimator
zbMATH Keywords: Cramér-von Mises type statistic
zbMATH Keywords: weighted empirical residual process
zbMATH Keywords: stationary
zbMATH Keywords: ergodic
zbMATH Keywords: influence curve
zbMATH Keywords: first- order autoregressive model
zbMATH Keywords: minimum distance estimator
zbMATH Keywords: asymptotically efficient estimator
zbMATH Keywords: goodness-of-fit tests of symmetry
reviewed by: Pedro A. Morettin
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1214/aos/1176350059
OpenAlex ID: W2030836760
DOI: 10.1214/AOS/1176350059
description: scientific article; zbMATH DE number 3982343