Partial likelihood inference for time series following generalized linear models

Fokianos, K. and Kedem, B. (2004) Partial likelihood inference for time series following generalized linear models. Journal of Time Series Analysis, 25 (2). pp. 173-197. ISSN 0143-9782

Full text not available from this repository.

Abstract

The present article offers a certain unifying approach to time series regression modelling by combining partial likelihood (PL) inference and generalized linear models. An advantage gained by resorting to PL is that the joint distribution of the response and the covariates is left unspecified, and furthermore, PL allows for temporal or sequential conditional inference with respect to a filtration generated by all that is known to the observer at the time of observation. Two real data examples illustrate the methodology.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Time Series Analysis
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
?? DEVIANCE LINK FUNCTIONLOGISTIC REGRESSIONSTOCHASTIC TIME‐DEPENDENT COVARIATESMARTINGALEPOISSON REGRESSIONAPPLIED MATHEMATICSSTATISTICS AND PROBABILITYSTATISTICS, PROBABILITY AND UNCERTAINTY ??
ID Code:
127888
Deposited By:
Deposited On:
03 Oct 2018 13:06
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Sep 2023 04:25