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Partial likelihood inference for time series following generalized linear models

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Partial likelihood inference for time series following generalized linear models. / Fokianos, K.; Kedem, B.
In: Journal of Time Series Analysis, Vol. 25, No. 2, 03.2004, p. 173-197.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fokianos, K & Kedem, B 2004, 'Partial likelihood inference for time series following generalized linear models', Journal of Time Series Analysis, vol. 25, no. 2, pp. 173-197. https://doi.org/10.1046/j.0143-9782.2003.00344.x

APA

Fokianos, K., & Kedem, B. (2004). Partial likelihood inference for time series following generalized linear models. Journal of Time Series Analysis, 25(2), 173-197. https://doi.org/10.1046/j.0143-9782.2003.00344.x

Vancouver

Fokianos K, Kedem B. Partial likelihood inference for time series following generalized linear models. Journal of Time Series Analysis. 2004 Mar;25(2):173-197. doi: 10.1046/j.0143-9782.2003.00344.x

Author

Fokianos, K. ; Kedem, B. / Partial likelihood inference for time series following generalized linear models. In: Journal of Time Series Analysis. 2004 ; Vol. 25, No. 2. pp. 173-197.

Bibtex

@article{45ffdb3d02c0486d997e5fc4dfba152a,
title = "Partial likelihood inference for time series following generalized linear models",
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.",
keywords = "Deviance , link function, logistic regression, stochastic time‐dependent covariates, martingale, Poisson regression",
author = "K. Fokianos and B. Kedem",
year = "2004",
month = mar,
doi = "10.1046/j.0143-9782.2003.00344.x",
language = "English",
volume = "25",
pages = "173--197",
journal = "Journal of Time Series Analysis",
issn = "0143-9782",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Partial likelihood inference for time series following generalized linear models

AU - Fokianos, K.

AU - Kedem, B.

PY - 2004/3

Y1 - 2004/3

N2 - 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.

AB - 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.

KW - Deviance

KW - link function

KW - logistic regression

KW - stochastic time‐dependent covariates

KW - martingale

KW - Poisson regression

U2 - 10.1046/j.0143-9782.2003.00344.x

DO - 10.1046/j.0143-9782.2003.00344.x

M3 - Journal article

VL - 25

SP - 173

EP - 197

JO - Journal of Time Series Analysis

JF - Journal of Time Series Analysis

SN - 0143-9782

IS - 2

ER -