Observation-driven generalized state space models for categorical time series

DOI10.1016/j.spl.2009.08.023zbMath1176.62090OpenAlexW2076261485MaRDI QIDQ1041704

J. Blot

Publication date: 4 December 2009

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2009.08.023

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Biochemistry, molecular biology (92C40)

Uses Software

• astsa
• itsmr

Cites Work

• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• Prediction and classification of non-stationary categorical time series
• Regression theory for categorical time series
• Regression models for nonstationary categorical time series: Asymptotic estimation theory
• Constructing First Order Stationary Autoregressive Models via Latent Processes
• REGRESSION MODELS FOR NON‐STATIONARY CATEGORICAL TIME SERIES
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