Monte Carlo Kalman filter and smoothing for multivariate discrete state space models

DOI10.2307/3315971zbMath0958.62093OpenAlexW1986432842MaRDI QIDQ4527905

Peter X.-K. Song

Publication date: 8 April 2001

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

Full work available at URL: https://semanticscholar.org/paper/249d48414875b093f125be543b5651fc7220a06e

zbMATH Keywords

Kalman filterstate space modelsbinomial time series

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Monte Carlo methods (65C05)

Related Items (4)

Phase I risk-adjusted Bernoulli chart in multistage healthcare processes based on the state-space model ⋮ State space mixed models for binary responses with scale mixture of normal distributions links ⋮ State space mixed models for longitudinal observations with binary and binomial responses ⋮ Discrete-response state space models with conditional heteroscedasticity: an application to forecasting the federal funds rate target

Uses Software

• Fahrmeir

Cites Work

• Unnamed Item
• Time series: theory and methods
• Multivariate statistical modelling based on generalized linear models. With contributions by Wolfgang Hennevogl
• The Calculation of Posterior Distributions by Data Augmentation
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• Posterior Mode Estimation by Extended Kalman Filtering for Multivariate Dynamic Generalized Linear Models
• A state space model for multivariate longitudinal count data
• Filtering and Smoothing Via Estimating Functions
• State‐space models for multivariate longitudinal data of mixed types

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