Partial likelihood analysis of a general regression model for the analysis of nonstationary categorical time series is presented, taking into account stochastic time dependent covariates. The model links the probabilities of each category to a covariate process through a vector of time invariant par
REGRESSION MODELS FOR NON-STATIONARY CATEGORICAL TIME SERIES
β Scribed by Ludwig Fahrmeir; Heinz Kaufmann
- Book ID
- 111039530
- Publisher
- John Wiley and Sons
- Year
- 1987
- Tongue
- English
- Weight
- 671 KB
- Volume
- 8
- Category
- Article
- ISSN
- 0143-9782
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Sequential procedures are proposed to estimate the regression parameters in a linear regression model with dependent residuals. The error process considered here is a linear process with unknown spectral density. The sequential point estimator for the regression parameters is based on the least-squa
A general class of sequential models for the analysis of ordered categorical variables ie developed and discussed. The models apply if the ordinal response may be subdivided into two or more meaningful sets of response categorim. The parametrizetion explicitly makes use of this subdivision. The mode