What question did this study set out to answer?

To establish a unified time series regression framework combining partial likelihood inference with generalized linear models.

March 1, 2004Journal of Time Series Analysis

Partial Likelihood Inference For Time Series Following Generalized Linear Models

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Authors

KFKonstantinos FokianosUniversity of Cyprus BKBenjamin KedemUniversity of Maryland, College Park

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Implication

Methodological study demonstrates partial likelihood inference for time series generalized linear models, suggesting robust sequential estimation without specifying full joint distributions.

Key Points

To establish a unified time series regression framework combining partial likelihood inference with generalized linear models.
Formulated a partial likelihood estimation framework for generalized linear time series models based on an evolving filtration of historical observations.
Applied the proposed statistical methodology to two empirical real-world time series datasets.
Demonstrated that partial likelihood enables valid sequential conditional inference while leaving the joint distribution of responses and covariates completely unspecified.
Showed through empirical examples that the combined framework provides robust regression estimation for complex, sequentially observed time series.

Cite This Study

Fokianos et al. (2004) studied this question.

synapsesocial.com/papers/6a1be07e26cb5670aa9d0a10 https://doi.org/10.1046/j.0143-9782.2003.00344.x

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Implication

Methodological study demonstrates partial likelihood inference for time series generalized linear models, suggesting robust sequential estimation without specifying full joint distributions.

Partial Likelihood Inference For Time Series Following Generalized Linear Models

Discussion

Member takes

Implication

Key Points

Cite This Study

Partial Likelihood Inference For Time Series Following Generalized Linear Models

Discussion

Member takes

Implication

Key Points

Cite This Study

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