Estimating and modeling spatio-temporal correlation structures for river monitoring networks

DOI10.1198/108571107X197977zbMath1306.62260OpenAlexW1986020362WikidataQ60453629 ScholiaQ60453629MaRDI QIDQ2259649

L. Clement, Olivier Thas

Publication date: 5 March 2015

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1198/108571107x197977

zbMATH Keywords

Kalman filtergeneralized least squaresECM algorithmintervention analysisriver water quality

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Ecology (92D40)

Related Items (2)

Intervention analysis of hurricane effects on~snail abundance in a tropical forest using long-term spatiotemporal data ⋮ S3T: A score statistic for spatiotemporal change point detection

Uses Software

• astsa

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• Estimation of parameterized spatio-temporal dynamic models
• An improved Akaike information criterion for state-space model selection
• Estimation, filtering, and smoothing in state space models with incompletely specified initial conditions
• Maximum likelihood estimation via the ECM algorithm: A general framework
• On the Asymptotic Efficiency of Feasible Aitken Estimators for Seemingly Unrelated Regression Models with Error Components
• Maximum likelihood estimation of models for residual covariance in spatial regression
• Linear Recursive Equations, Covariance Selection, and Path Analysis
• On closed testing procedures with special reference to ordered analysis of variance
• Semiparametric Models for Longitudinal Data with Application to CD4 Cell Numbers in HIV Seroconverters
• A new look at the statistical model identification

This page was built for publication: Estimating and modeling spatio-temporal correlation structures for river monitoring networks