Quasi-Likelihood Sample Clauses

Quasi-Likelihood. When dealing with skewed data, whether pooled or individual, it may be helpful to fit a quasi-likelihood (QL) model. The concept of quasi-likelihood was introduced by Xxx- xxxxxxx (1974) as an alternative to maximum likelihood estimation when the underlying distribution is unknown. Quasi-likelihood requires specification of only the first and sec- ond moments; instead of maximizing a fully-specified log-likelihood, the quasi-likelihood is maximized, where the contribution of observation zi is: K(zi, µi) = zi − t dt + c(z ), ∫ i µi zi φV (t) where µi = E(zi; β) is a known function of some parameters β = (β1, . . . , βp), often assumed to be the coefficients in a regression setting. V (•) is a known function of the mean, φ is a dispersion parameter, and c(•) is some function of zi not dependent on µi. Xxxxxxxxxx argues that K has properties similar to a log-likelihood, namely:
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Related to Quasi-Likelihood

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