State‐space models for multivariate longitudinal data of mixed types

DOI10.2307/3315747zbMath0877.62083OpenAlexW2087638772MaRDI QIDQ5691195

Søren Lundbye-Christensen, Bent Jørgensen, Li Sun, Peter X.-K. Song

Publication date: 2 December 1997

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3315747

zbMATH Keywords

smootherKalman filterestimating equationresidual analysisexponential dispersion modeltime-varying covariatesdynamic generalized linear modelTweedie class

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Generalized linear models (logistic models) (62J12)

Related Items (13)

Monte Carlo Kalman filter and smoothing for multivariate discrete state space models ⋮ A new approach to modeling positive random variables with repeated measures ⋮ Multivariate Time-Series Analysis With Categorical and Continuous Variables in an Lstr Model ⋮ Unit gamma regression models for correlated bounded data ⋮ Stationary state space models for longitudinal data ⋮ Distance-based approach in univariate longitudinal data analysis ⋮ Generating dependent random numbers with given correlations and margins from exponential dispersion models ⋮ Analysis of correlated Birnbaum-Saunders data based on estimating equations ⋮ An introduction to Bent Jørgensen's ideas ⋮ A statistical model for under- or overdispersed clustered and longitudinal count data ⋮ Extending the State-Space Model to Accommodate Missing Values in Responses and Covariates ⋮ Multivariate count autoregression ⋮ Unnamed Item

Uses Software

• Fahrmeir

Cites Work

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• Longitudinal data analysis using generalized linear models
• On Kalman filtering, posterior mode estimation and Fisher scoring in dynamic exponential family regression
• The diffuse Kalman filter
• Multivariate statistical modelling based on generalized linear models. With contributions by Wolfgang Hennevogl
• Smoothing and Interpolation with the State-Space Model
• A regression model for time series of counts
• Partial non-Gaussian state space
• APPROXIMATE FILTERING OF PARAMETER DRIVEN PROCESSES
• Monte Carlo EM Estimation for Time Series Models Involving Counts
• The simulation smoother for time series models
• Informative Drop-Out in Longitudinal Data Analysis

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