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Cited by 431

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Markov Regression Models for Time Series: A Quasi-Likelihood Approach

Zeger, Scott L., Qaqish, Bahjat·1988·Biometrics

Abstract

This paper discusses a quasi-likelihood (QL) approach to regression analysis with time series data. We consider a class of Markov models, referred to by Cox (1981, Scandinavian Journal of Statistics 8, 93-115) as "observation-driven" models in which the conditional means and variances given the past are explicit functions of past outcomes. The class includes autoregressive and Markov chain models for continuous and categorical observations as well as models for counts (e.g., Poisson) and continuous outcomes with constant coefficient of variation (e.g., gamma). We focus on Poisson and gamma data for illustration. Analogous to QL for independent observations, large-sample properties of the regression coefficients depend only on correct specification of the first conditional moment.

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Metadata

Authors
Zeger, S. L., & Qaqish, B.
Venue
Biometrics
Publisher
JSTOR
Published
1988-12
Volume
44
Issue
4
Pages
1019
ISSN
0006-341X (ISSN-L)
OpenAlex
W2009632219
Cited by
431
References
2
FWCI
4.82
Type
journal article
Language
en
Open access
closed

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