Regression models for binary time series with gaps

DOI10.1016/j.csda.2008.01.019zbMath1452.62652OpenAlexW2117460532MaRDI QIDQ1023754

Bernhard Klingenberg

Publication date: 16 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.01.019

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Generalized linear models (logistic models) (62J12)

Related Items

A PARAMETER‐DRIVEN LOGIT REGRESSION MODEL FOR BINARY TIME SERIES ⋮ The distribution of the maximum of a first-order moving average: The discrete casex

Uses Software

• Fahrmeir

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A pairwise likelihood approach to estimation in multilevel probit models
• Time series of count data: Modeling, estimation and diagnostics
• Pairwise likelihood inference for ordinal categorical time series
• Multivariate regression analysis of panel data with binary outcomes applied to unemployment data
• Conditional and marginal models: another view (with comments and rejoinder)
• A regression model for time series of counts
• Lorelogram: A Regression Approach to Exploring Dependence in Longitudinal Categorical Responses
• Analysis of multivariate probit models
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Posterior distribution of hierarchical models using CAR(1) distributions
• Generalized Autoregressive Moving Average Models
• On autocorrelation in a Poisson regression model
• Bayesian Predictive Inference for Time Series Count Data
• Bayesian Measures of Model Complexity and Fit
• Mixed Models
• Partial Likelihood Inference For Time Series Following Generalized Linear Models
• Monte Carlo EM Estimation for Time Series Models Involving Counts
• Time Series Models Based on Generalized Linear Models: Some Further Results
• Variance Components Testing in the Longitudinal Mixed Effects Model
• Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives
• Conditional Akaike information for mixed-effects models
• Bayesian analysis of a time series of counts with covariates: an application to the control of an infectious disease
• Multivariate statistical modelling based on generalized linear models.
• Bayesian model determination for binary time-series data with applications
• Deviance information criteria for missing data models