A recursive approach for estimating missing observations in an univariate time series

DOI10.1080/03610929608831824zbMath0900.62489OpenAlexW1971175034MaRDI QIDQ4337253

Jorge Martinez, Fabio H. Nieto

Publication date: 19 May 1997

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610929608831824

zbMATH Keywords

missing observationsARIMA modelsrecursive linear estimationminimum-mean-square error

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (1)

Sensitivity of the portmanteau statistic in time series modeling

Cites Work

• Unnamed Item
• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• Signal extraction from nonstationary time series
• Estimation, control, and the discrete Kalman filter
• Time series: theory and methods.
• A nonlinear time series model and estimation of missing observations
• A Note on the Estimation of Missing Values in Time Series
• Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data
• Estimation of the interpolation error variance and an index of linear determinism
• Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
• Exact likelihood of vector autoregressive-moving average process with missing or aggregated data
• Estimation, Prediction, and Interpolation for Nonstationary Series with the Kalman Filter

This page was built for publication: A recursive approach for estimating missing observations in an univariate time series