Penalized likelihood estimation and iterative Kalman smoothing for non-Gaussian dynamic regression models
- 1 May 1997
- journal article
- Published by Elsevier in Computational Statistics & Data Analysis
- Vol. 24 (3) , 295-320
- https://doi.org/10.1016/s0167-9473(96)00064-3
Abstract
No abstract availableKeywords
This publication has 13 references indexed in Scilit:
- Applied state space modelling of non-Gaussian time series using integration-based Kalman filteringStatistics and Computing, 1994
- On Gibbs sampling for state space modelsBiometrika, 1994
- State Space Modelling of Cross-Classified Time Series of CountsInternational Statistical Review, 1992
- Posterior Mode Estimation by Extended Kalman Filtering for Multivariate Dynamic Generalized Linear ModelsJournal of the American Statistical Association, 1992
- A Monte Carlo Approach to Nonnormal and Nonlinear State-Space ModelingJournal of the American Statistical Association, 1992
- Integration-based Kalman-filtering for a dynamic generalized linear trend modelComputational Statistics & Data Analysis, 1992
- On kalman filtering, posterior mode estimation and fisher scoring in dynamic exponential family regressionMetrika, 1991
- A fast algorithm for signal extraction, influence and cross-validation in state space modelsBiometrika, 1989
- Penalized Likelihood for General Semi-Parametric Regression ModelsInternational Statistical Review, 1987
- Accurate Approximations for Posterior Moments and Marginal DensitiesJournal of the American Statistical Association, 1986