Methodologies for the estimation of missing observations in time series
type: Item
instance of: scholarly article
title: Methodologies for the estimation of missing observations in time series
zbMATH Open document ID: 0601.62109
DOI: 10.1016/0167-7152(87)90028-9
author: Osvaldo Ferreiro
published in: Statistics \& Probability Letters
publication date: 1987-01-01T00:00:00Z
review text: The article discusses different alternatives for the estimation of missing observations in stationary time series following autoregressive- moving average (ARMA) models. The occurrence of missing observations is quite common in time series and in many cases it is important to estimate them. The article offers an array of estimation alternatives to help the practitioner.
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 3969947
zbMATH Keywords: Kalman-Bucy filter
zbMATH Keywords: PEM algorithm
zbMATH Keywords: pseudo-expectation maximization
zbMATH Keywords: estimation of missing observations
zbMATH Keywords: stationary time series
zbMATH Keywords: autoregressive-moving average
zbMATH Keywords: ARMA
MaRDI profile type: Publication
full work available at URL: 0167-7152(87)90028-9
OpenAlex ID: W2061824698
cites work: Q3847819
cites work: Q3875172
cites work: Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
cites work: Q3223758
description: scientific article; zbMATH DE number 3969947